374results about How to "Reduce false positives" patented technology

The invention discloses an industrial network fault intelligent diagnosis method and system based on multi-source data, and the method comprises the steps: synchronously collecting data from a plurality of data sources of an industrial network, and extracting a time sequence statistical feature, a flow entropy feature and a protocol conformity feature to form a multi-dimensional feature vector; establishing a dynamic baseline model by adopting a sliding window online learning method, and calculating a comprehensive anomaly score for anomaly detection; the fault suspicion degree is calculated based on the equipment incidence matrix and the fault propagation model to realize fault source positioning; carrying out fault type identification and root cause analysis by adopting Bayesian reasoning and a knowledge rule base; and outputting a structured diagnosis report containing the fault source, the type, the root cause and the disposal suggestion. According to the invention, early warning, accurate positioning and intelligent diagnosis of industrial network faults are realized, and the operation and maintenance efficiency, safety and reliability of the industrial control network are significantly improved.

The invention relates to the technical field of intelligent monitoring of power equipment, in particular to an intelligent circuit breaker state monitoring method and system. The method comprises the steps that unified time sequence modeling is carried out on multi-dimensional operation data of the circuit breaker, periodic behavior fragments with internal rhythm characteristics are extracted, state feature vectors based on historical behavior self-reference are constructed, and the state feature vectors are mapped to a continuous interpretable state space to achieve dynamic visual expression of the operation state; on the basis, the state deviation intensity and the change direction consistency are quantified, so that progressive identification and early warning from normal operation to an abnormal state are realized; and combined prediction is further carried out in combination with a state trajectory and an abnormal index, a risk quantification result and an active intervention strategy are generated, and model adaptive optimization is realized through closed-loop feedback. According to the invention, intelligent operation and maintenance closed loop from real-time monitoring and abnormal early warning to predictive maintenance is completed.

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 belongs to the technical field of new energy automobile steering systems, and discloses a new energy automobile electric hydraulic power-assisted steering system and a control method thereof.The system integrates a double-source redundant hydraulic power unit, a hydraulic adjusting unit, a multi-dimensional working condition sensing unit and an intelligent main controller; the multi-dimensional working condition sensing unit is integrated with a road adhesion coefficient detection module and a battery remaining capacity detection module. The control method is layered look-ahead matching control and is executed by an intelligent main controller in parallel: a power-assisted decision-making layer divides a three-stage power-assisted mode according to multi-dimensional perception information, and limits a steering angular velocity when power-assisted is enhanced on a low-attached road surface; the energy optimization layer predicts and implements prospective energy storage and pump rotating speed self-adaptive adjustment based on the intention of a driver; and the fault diagnosis layer realizes rapid fault identification and redundancy switching through multi-parameter collaborative diagnosis. The contradiction of high energy consumption, response delay and weak safety guarantee of a traditional system is solved, and power-assisted steering which is safe, energy-saving, fast in response and high in adaptability is achieved.

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 relates to the technical field of campus safety protection, in particular to a method and system for preventing campus bullying by using a laser radar imaging technology, and the method comprises the following steps: obtaining 3D point cloud data of an environment in a monitoring region preset in a campus, and collecting sound data in the monitoring region; processing the 3D point cloud data, generating a three-dimensional environment image and extracting a first behavior feature; processing the sound data, and extracting a second behavior feature; performing multi-modal fusion on the first behavior feature and the second behavior feature to generate a unified behavior feature vector; based on the behavior feature vector, identifying whether a bullying behavior exists in the current scene, and calculating a corresponding behavior risk score; according to the method and the device, whether the preset alarm threshold value is reached or not is judged, and if yes, corresponding alarm and early warning operation is triggered, so that non-intrusive active monitoring of non-appearance privacy leakage of the campus secret corner is realized, and the problems of privacy limitation and blind areas of traditional video monitoring are solved.

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 vulnerability early warning method and system based on asset fingerprint driving. The method comprises the following steps: acquiring vulnerability information data from a plurality of vulnerability information sources; asset fingerprint information of assets in the target system is collected; based on semantic analysis, version comparison and a fingerprint matching algorithm, performing fine-grained association matching on the vulnerability information data and asset fingerprint information, and identifying target assets affected by vulnerabilities; performing influence range analysis, risk level evaluation and repair priority ranking on the target assets to generate a vulnerability influence analysis report; based on the vulnerability impact analysis report, sending an early warning notification to a related person in charge, and automatically generating an electronic work order; the processing state of the electronic work order is tracked, the repair progress is recorded, and full-life-cycle closed-loop management of vulnerabilities from recognition to repair is achieved. The missing report rate and the false report rate are effectively reduced, and compared with manual matching, the full-automatic mode can form a complete closed loop, and the early warning efficiency is improved.

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.

The application discloses a pipeline threat behavior identification method and device and a storage medium, and relates to the technical field of machine vision. The method comprises the following steps: acquiring a to-be-detected video photographed by unmanned aerial vehicle inspection; performing target detection on image frames of the to-be-detected video, determining target detection boxes of detection objects in the image frames and target categories of the detection objects; performing trajectory tracking on the target detection boxes to obtain target trajectories of the detection objects; extracting behavior features of the detection objects, wherein the behavior features comprise morphological change features of the detection boxes and trajectory features of the target trajectories of the detection objects; and identifying pipeline threat behaviors of the detection objects based on the target categories, the behavior features of the detection objects and a pre-trained classification model. The method can effectively detect small targets such as personnel and vehicles, effectively determine threat behaviors such as personnel loitering, personnel digging and mechanical digging, and the algorithm has the light-weight feature.

The invention discloses a rapid screening and detecting method for vegetable pesticide residues, a multi-dimensional judgment basis is provided for pesticide residue detection by combining a PCR amplification result with the inhibition rate of acetylcholin esterase and butyrylcholin esterase, different types of pesticides may induce different gene expression changes, and the detection result is accurate. According to the rapid screening and detecting method for the pesticide residues in the vegetables, the accuracy of a detection result is improved, meanwhile, the variety of the pesticide residues in the vegetables can be accurately determined, the defect that specific pesticide varieties are difficult to distinguish by purely relying on an enzyme inhibition method is overcome, and the detection result is more targeted and accurate.

The present application belongs to the technical field of data processing, and discloses a gait visual feedback anti-falling safety warning method for a rehabilitation treadmill; including, converting clinical characteristic data of a patient into a network structure gating instruction set; collecting multi-modal original data in a patient's movement process in real time; inputting the multi-modal original data into a mechanism constraint engine to extract biomechanical mechanism characteristic data, neural mechanism characteristic data and cognitive mechanism characteristic data; based on the network structure gating instruction set, dynamically reconstructing internal node connection paths and data flow weights of a preset basic fusion network to form an individualized dynamic fusion model; outputting three-dimensional risk attribution data; according to the proportion distribution of each dimension in the three-dimensional risk attribution data, driving a visual feedback interface of the rehabilitation treadmill to perform differentiated anti-falling warning display of the corresponding dimension; and realizing gait visual feedback anti-falling safety warning for the rehabilitation treadmill.

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 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 relates to the technical field of high-speed aircraft health management, and particularly discloses an interpretable aircraft state sensing and predicting system and method. The method comprises the following steps: constructing and training a lightweight anomaly diagnosis network with an expansion perception layer; aircraft dynamics, kinematics and fault mechanism formulas are used as soft constraints to be embedded into the future state estimation network; an SHAP interpretation framework is used for sorting the importance of the input features, and a network structure is optimized accordingly; and finally, accurate diagnosis of abnormal types and positions and rapid prediction of aircraft states at a plurality of moments in the future are realized. According to the method, by introducing three innovation points of an expansion perception layer, physical information embedding and feature importance sorting, the diagnosis accuracy and the model interpretability are remarkably improved, and the high-performance calculation requirement in a scene with limited calculation power is met.

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.

This invention provides a method for detecting sensor anomalies in nuclear power plants. The method includes: constructing a principal component analysis model, calculating the Q-statistic, and determining a first threshold; training a Bayesian long short-term memory neural network model and calibrating its prediction interval; calculating a second and third threshold based on the predicted value and uncertainty output by the calibrated model; inputting test data into the two models to obtain the Q-statistic, predicted value, and uncertainty, and comparing them with the corresponding thresholds to obtain three judgment results; and making a final judgment on the three results using a logical judgment table. This invention integrates spatiotemporal characteristics and uncertainty assessment, achieving high-precision and high-reliability sensor anomaly detection with only normal operating data, exhibiting high reliability and robustness.

The application discloses a kind of vehicle biological invasion intelligent monitoring methods based on multi-sensor fusion, it is related to vehicle-mounted intelligent safety monitoring and response technical field, including the multi-modal data of vehicle key area is synchronously collected by sensor, and pretreatment is carried out, obtain the multi-modal data after pretreatment;Feature extraction is carried out to the multi-modal data after pretreatment, extract frequency domain feature vector, voiceprint matching degree and vibration energy increment, and the confidence index of biological invasion is comprehensively calculated;According to confidence index stratified trigger response strategy, complete the vehicle biological invasion intelligent monitoring and response based on multi-modal perception.The application significantly improves the detection sensitivity and identification accuracy of small biological invasion in vehicle key area;Give consideration to low-power on-line operation and real-time protection needs, and reduce false alarm and miss risk by stratified response.

The application discloses an improved BGRU-based dry-type transformer fault intelligent diagnosis method and system, which is suitable for an online monitoring scene of a cooling fan, continuously collects a load current sequence and a winding temperature sequence, and obtains a cooling fan start-stop state. The method determines an effective thermal time constant based on the winding temperature sequence and in combination with the cooling fan start-stop state, and determines a thermal response blind area based on the effective thermal time constant and a preset proportion coefficient, wherein the preset proportion coefficient is selected according to a load current transient mutation direction. The thermal response blind area is used to constrain the improved BGRU model to handle the confusion of the overload state and the winding short-circuit fault, so that the two types of faults do not occur in the early diagnosis stage in the unidentifiable stage, thereby reducing false positives and keeping the diagnosis output available.

The application discloses a method for anti-electricity stealing based on abnormal connection of user single-phase electric meter, and relates to the technical field of electric power metering, which comprises the following steps: installing sampling units on the incoming line side and the outgoing line side of the user single-phase electric meter, synchronously collecting single-phase voltage, current, neutral line current and temperature and humidity signals of the meter box, and generating synchronous electrical data sequences through standardization and time stamp alignment. Single-phase voltage and current fundamental phase angles are calculated based on voltage and current instantaneous value sequences, and a phase angle difference map is constructed. The effective value of the neutral line current is calculated, and an abnormal flag sequence is generated in combination with a normal load reference range. The environmental temperature and humidity abnormal fluctuation period is extracted. The above-processed features are input into a connection mode recognition model to identify suspected electricity stealing connection mode categories, improve the accuracy of meter connection abnormality recognition, and reduce misjudgment. The method can effectively identify meter connection abnormalities and improve the accuracy of electricity stealing identification.

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 application relates to a mine energy consumption monitoring and analysis method based on multi-source data fusion. The method collects multi-source heterogeneous energy consumption data and granularity data from multiple production links of the whole process of mine production, merges and aligns the data with production events as alignment anchors, and correlates the data of the same batch of ores between links, converts the energy consumption and material consumption data into normalized energy consumption values under a unified reference according to energy consumption equivalents, establishes an energy consumption transmission relationship between upstream granularity and downstream normalized energy consumption values by taking granularity as an energy consumption coupling variable between adjacent production links, determines the reverse attribution of downstream high-energy-consumption-link trailing granularity to upstream production links, and obtains the energy consumption transmission contribution proportion of the upstream production link to the high-energy-consumption link. The application can trace high energy consumption of downstream processes to upstream process causes, so that energy-saving reconstruction can be targeted.

This invention relates to the field of pose analysis technology, and discloses a method, system, terminal, and storage medium for human body localization and pose estimation based on spatial fingerprint tensors. The method includes: acquiring the target's original echo signal data and performing near-field spherical wave modeling to construct a spatial fingerprint tensor; performing feature decomposition on the spatial fingerprint tensor to extract various features; using these features, performing human target detection and localization through a lightweight tensor network classifier to distinguish between human and non-human targets; for detected human targets, further analyzing the spatiotemporal variation pattern of their spatial fingerprint tensor; and combining this with a human kinematic model to estimate the human pose. This invention abandons the traditional process of first generating point clouds, directly using the original echo signal to construct a spatial fingerprint tensor, achieving accurate localization of human targets through tensor decomposition and feature extraction, using polarization scattering features to distinguish human body materials, and combining micro-motion features to reliably detect human bodies, reducing false alarms.

The invention relates to the technical field of coal mine fire prediction, and discloses a coal mine fire accurate early warning method based on a dynamic comprehensive prevention and control technology, and the method comprises the steps: receiving multi-source environment sensing data of a mine monitoring end, and then carrying out the preliminary screening through combining with a coal mine global risk database, and obtaining a first risk region set; when the set meets an early warning trigger condition, a roadway risk situation map is called and sent to a monitoring center end, and the roadway risk situation map comprises a risk layout plane corresponding to the roadway network structure; then determining a risk unit area based on the division information of the monitoring center end on the risk layout plane, and determining a risk factor type according to the labeling information of the risk unit area; then calculating a comprehensive risk assessment coefficient; and finally, generating a fire risk early warning sequence according to a descending order of the coefficients, and sending the fire risk early warning sequence to a monitoring center end, thereby realizing accurate and low-false-alarm fire early warning.

The application discloses an AI fault diagnosis and auxiliary decision method and system for a lithium battery, and relates to the technical field of lithium battery safety monitoring.The application continuously collects transient stress waves generated by internal bubble rupture through a piezoelectric acoustic emission sensor, extracts the main ridge line center frequency, energy attenuation time constant and duration of each acoustic emission event by using adaptive time-frequency analysis and time-frequency ridge line tracking, constitutes a feature vector, inputs the feature vector into a pre-trained deep learning AI event classification model, accurately distinguishes normal electrochemical reaction bubble events from electrolyte boiling bubble events, counts the boiling bubble event density in a sliding time window, combines an adaptive safety threshold and a fault severity factor to determine whether a local hot area and a gas film have been formed inside the battery cell, and divides the fault level, captures the acoustic emission characteristics of electrolyte boiling, realizes early warning of thermal runaway, and takes into account the timeliness of safety protection and the controllability of vehicle operation.

The invention provides a mobile maintenance vehicle rear-end collision risk real-time early warning method and system based on a front time window, and relates to the technical field of intelligent traffic. The method comprises the steps of integrating sensors such as a GNSS / IMU, a millimeter wave radar and a binocular camera on a mobile maintenance vehicle, obtaining information such as the relative distance, the speed, the acceleration and the lane position of a backward coming vehicle and the maintenance vehicle, constructing a time sequence prediction model which takes historical fragments as input and applies front time window constraint to data nearby the current moment, and predicting the time sequence of the current time. Relative movement in a future short time period is predicted, safety indexes such as collision time TTC and required deceleration DRAC are calculated, threshold values are corrected in combination with road alignment, longitudinal slopes and visibility, and a directional horn is used for achieving rear-end collision early warning. According to the method, the false alarm rate and the missing alarm rate can be reduced in complex scenes such as curves, long slopes and low visibility, the early warning accuracy and robustness are improved, and rear-end collision accidents of mobile maintenance operation are reduced.

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 discloses intelligent online monitoring equipment applied to a cable joint, which belongs to the technical field of cable detection and comprises a transmission optical fiber, a fiber bragg grating sensor, an optical fiber demodulator, a data processing unit and an intelligent early warning unit. The transmission optical fiber is used for monitoring signal transmission; the sensor is installed at a key temperature measurement part of a cable joint and is connected in series through optical fiber fusion to form a sensing network. The optical fiber demodulator realizes multi-joint positioning monitoring and wavelength offset acquisition through a wavelength division multiplexing or time division multiplexing technology; the data processing unit converts the wavelength offset into temperature variation; and the intelligent early warning unit adopts a convolutional neural network, a bidirectional long-short-term memory network and a self-attention mechanism mixed model to realize fault grading early warning and temperature rise trend advanced pre-judgment. The cable joint fault early warning system does not need field power supply, is high in anti-electromagnetic interference capability, can realize all-weather online monitoring and accurate positioning, and effectively improves the accuracy and timeliness of cable joint fault early warning.