154 results about "Abnormal positions" patented technology

The invention relates to the technical field of voiceprint detection, and discloses an abnormal voiceprint detection system and method based on a pump characteristic spectrum, and the system comprises a collection module which sets a sampling rate, generates a voiceprint index for a pump according to an analysis result, and determines an abnormal signal; the processing module recognizes the voiceprint indexes, obtains the characteristic frequency spectrum of the pump machine based on the processing result, determines the abnormal position of the pump machine, and determines the abnormal type of the pump machine according to the abnormal voiceprint characteristics; and the verification module compares the abnormal voiceprint characteristics with historical voiceprint data, determines the similarity of abnormal voiceprints, re-determines the abnormal type of the pump machine by adopting a clustering algorithm when the abnormal type is judged to be inaccurate, and determines the abnormal prediction type of the pump machine based on a convolutional neural network model when the abnormal position is located on the wall surface of the pump machine. Verifying whether the exception type is accurate according to the exception prediction type; and the early warning module determines a graded alarm according to the abnormal position and the verified abnormal type. The method has the characteristics of good detection and analysis.

The invention discloses a fault analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining first burying point information corresponding to an abnormal event if the vehicle detects that the abnormal event of a burying point is triggered, the first burying point information comprises at least one of a subsystem type corresponding to the abnormal event, an application module corresponding to the abnormal event, an abnormal type corresponding to the abnormal event, an information domain corresponding to the abnormal event, a fault identifier corresponding to the abnormal event, an abnormal name corresponding to the abnormal event and an abnormal position corresponding to the abnormal event; and sending the first burying point information to a server, so that the server determines the fault information corresponding to the abnormal event according to the first burying point information, thereby improving the vehicle fault problem determination rate.

The invention discloses an anomaly detection method, system and device based on a multi-mode visual large model and a medium, and the method comprises the steps: carrying out the shooting of an image at a designated point through an inspection device, comparing a template image in a normal state with a detection image collected in real time, whether abnormity exists or not and the position of the abnormity in an image are judged through the following steps that S1, the image is registered; s2, identifying a target in the image by using an open set target detection large model so as to detect various abnormal parts; s3, performing image segmentation by using the large segmentation model; s4, performing IOU comparison on contours generated by segmentation of the template image and the to-be-detected image, removing part of differences, performing area-by-area analysis, and setting the contours with IOU smaller than a specified threshold value as possible abnormal positions; and S5, carrying out similarity comparison on the different contours, removing contours with the similarity greater than a preset threshold value, and taking the rest contours as abnormal areas. The method does not need to perceive a specific abnormal type in advance, and has the capability of identifying and segmenting multiple types of targets.

The invention discloses a monitoring and early warning method based on intelligent wearing, and aims to improve the safety of a user. The method comprises the following steps: continuously collecting original data streams from various built-in sensors through intelligent wearable equipment, and reflecting the state, posture, position and environment information of a user in real time; and carrying out real-time preprocessing, time-space synchronization and multi-modal fusion on the data to construct unified data representation. Based on this, analysis and feature extraction are performed, key indicators are calculated and compared with threshold values, and preset safety or auxiliary events are identified and confirmed in real time, the events at least including user tumble, location abnormality, external threats or active help seeking of the user. And according to the identified event type, severity and context, automatically selecting and triggering a corresponding execution module built in the equipment to execute a preset response action. According to the invention, various danger or help-seeking scenes can be accurately identified, and timely and effective automatic response is provided.

The invention discloses a test link abnormity positioning device, system, method, equipment, medium and program, and relates to the technical field of computers, and the method comprises the steps that when a test link is abnormal, a processing component writes a first enabling bit into a first register of each node device, each node device responds to the first enabling bit to enter a low-power-consumption mode, and the first enabling bit enters a low-power-consumption mode; according to the embodiment of the invention, each node device triggers link retraining in the low-power-consumption mode to expose the unstable position of the test link in the low-power-consumption conversion process, and determines the abnormal position of the link according to the flag bit state of the second register, so that the troubleshooting efficiency and the abnormality positioning precision are improved, and the time and the labor cost are saved. Therefore, the problems of low working efficiency and high time and labor cost caused by the fact that the abnormal problem cannot be effectively solved in time when the abnormal position of the link is checked manually in the prior art are solved.

The invention belongs to the technical field of smoothness detection of glass guide rail lifting systems, and discloses a performance detection and analysis method of a guide rail glass lifting system. According to the method, the noise data, the output current and the vibration time domain signal are subjected to data analysis to construct the smoothness comprehensive evaluation matrix, the system state is comprehensively evaluated from multiple aspects of frequency band energy, current fluctuation, vibration frequency and the like, the operation anomaly characteristics are comprehensively covered, the anomaly recognition accuracy is improved, and the system reliability is improved. A scientific basis can be provided for performance quantitative evaluation, and the one-sidedness of a single parameter is avoided. When the smoothness of the guide rail glass lifting system is judged to be abnormal, the abnormal part can be accurately recognized based on the noise spectrum mapping relation, meanwhile, the specific abnormal position is positioned in combination with current, vibration signals and position-time mapping, double positioning of the part type and the physical position is achieved, manual blind troubleshooting is avoided, and the working efficiency is improved. And the fault diagnosis efficiency and the maintenance pertinence are improved.

The invention relates to a product test repairing method and system, and relates to the technical field of product testing, and the method comprises the steps: obtaining a test position and a test parameter; when the test parameter is inconsistent with the reference parameter, defining the test position as an abnormal position, and calculating the difference between the test parameter and the reference parameter as an abnormal parameter; when the abnormal parameter is a positive value, controlling the cutting device to cut according to the abnormal parameter according to the test position; according to the abnormal parameters, air suction power is determined, and a recovery device is controlled to recover the cut materials; when the abnormal parameter is a negative value, the specification of the recycled material is obtained; determining whether the recycled material specification is greater than the abnormal specification; if yes, the splicing shape is determined according to the specification of the recycled material; controlling a repairing device to repair according to the splicing shape and the abnormal position; and if not, controlling the repairing device to repair according to the repairing material, controlling the polishing device to polish the product and obtaining the test parameters again. The method has the effect of reducing energy consumption.

The invention discloses a ship maintenance inspection robot and a use method, and relates to the technical field of ship maintenance inspection robots. Through mutual cooperation of the telescopic springs and the anti-collision plates, it is conveniently guaranteed that in the collision process of the robot, impact on a robot body is reduced, the use safety of the robot is improved, and then the function of protecting the robot and ship equipment is achieved; a linear relation model of obstacle length, width, walkway width and passing time is constructed to quantify the influence of the obstacle on the passing efficiency, and the limitation that only the route length is used as a single optimization index is avoided; the actual passing time of each route is calculated based on historical inspection data, the consumed time of the optimal route and the consumed time of the to-be-selected route are dynamically compared, real-time adjustment of the route priority is achieved, and the inspection efficiency in a complex environment is improved; and the distribution rule of abnormal positions is judged by using a linear correlation coefficient, and if abnormal points are linearly arranged, the corrosion possibility is eliminated, so that the misjudgment rate is reduced.

The invention discloses an AI substitute inspection method and system based on multi-modal perception, and relates to the technical field of intelligent detection. According to the method, images, audios, environmental parameters and equipment state data of an inspection area are synchronously collected through a multi-modal sensor array, and after space-time alignment preprocessing, multi-modal features are extracted and input into a pre-trained fusion model to generate unified feature representation. The fusion model adopts a Transform architecture to realize cross-modal feature interaction, an anomaly detection algorithm is used to identify and classify anomalies, a confidence coefficient is calculated in combination with an evidence theory, and finally an inspection report containing anomaly positions, types and processing suggestions is generated. According to the invention, the information limitation of single-mode inspection is solved, the detection accuracy and robustness in a complex environment are improved, unmanned inspection in a high-risk scene is realized, the labor cost is reduced, the safety is guaranteed, and the method is suitable for automatic inspection in the fields of industrial equipment, public facilities and the like.

The invention discloses an intelligent monitoring method and system for an underground pipe network based on optical fiber sensing. A multi-dimensional monitoring system of a monitoring space is constructed based on a corresponding optical fiber sensor, a camera module and a sound module; the corresponding abnormal nodes are determined according to the multiple monitoring parameters of the space monitoring points, and the accuracy of a multi-dimensional monitoring system and the accuracy of intelligent monitoring of the underground pipe network are guaranteed. The abnormal position is rechecked according to the comparison determined by the multiple parameters matched with the fixed-point monitoring and the multiple parameters matched with the peripheral monitoring, and the corresponding regulation and control logic is triggered based on the abnormal position; according to the method, the corresponding anomaly combination is constructed based on the multiple monitoring parameters corresponding to the abnormal position, the corresponding anomaly type is determined according to identification of the anomaly combination, intelligent regulation and control of the underground pipe network are triggered based on the anomaly type and the corresponding regulation and control logic, the intelligent regulation and control effect of the underground pipe network is guaranteed, and accurate control over the abnormal position is achieved.

The invention relates to the technical field of speed reducers, and particularly discloses a speed reducer fault monitoring method and system, and the method comprises the steps: obtaining multi-source data and working condition parameters; preprocessing the multi-source data; extracting a multi-dimensional feature vector from the preprocessed multi-source data according to the working condition parameters; calculating the deviation degree between the collaborative change condition of the multi-dimensional feature vector and the reference baseline; judging whether early abnormality occurs according to the deviation degree; when the early abnormality occurs, analyzing the type of an abnormal feature inducing deviation in the multi-dimensional feature vector, and determining an abnormal type and / or an abnormal part according to the type of the abnormal feature; according to the method, multi-source heterogeneous data acquisition and multi-dimensional feature extraction based on working condition parameters are combined, and a deviation degree calculation method based on a multi-dimensional feature collaborative change reference baseline is introduced to detect early abnormality, so that the problem that weak early fault signals are difficult to recognize in a traditional method is solved.

The invention relates to a robot cooperative task allocation method and system, and relates to the field of task allocation optimization, and the method comprises the steps: carrying out basic monitoring, obtaining an implicit abnormal position when an abnormality occurs, and carrying out the prediction to obtain a plurality of implicit abnormal angles and a plurality of angle probabilities; generating a plurality of adaptation coefficients and a plurality of cost coefficients according to the plurality of angle probabilities; and a plurality of real-time positions and a plurality of real-time angles of the plurality of monitoring robots are obtained, task optimization distribution is performed according to the hidden abnormal positions, the plurality of adaptation coefficients and the plurality of cost coefficients according to the sequence of the probabilities of the plurality of angles from large to small, and a plurality of monitoring tasks are obtained for verification monitoring. According to the invention, the technical problems of low monitoring efficiency, insufficient accuracy and waste of monitoring resources due to the fact that a traditional method cannot accurately allocate inspection tasks according to actual monitoring requirements and the current state of the robot can be solved; the scheduling cost can be effectively reduced, the accuracy and efficiency of anomaly detection can be improved, and efficient and accurate factory inspection can be realized.

The invention discloses a sound barrier anomaly recognition method and device based on an anomaly detection model, and relates to the field of anomaly detection. In the method, a spatio-temporal data matrix is constructed according to a timestamp, spatial coordinates of a sensor and multi-dimensional data, and a preset state feature vector is constructed according to the timestamp, environmental data and current traffic flow data; constructing a sensor topological graph according to the spatio-temporal data matrix, obtaining a spatio-temporal feature vector of each time step according to the sensor topological graph, and determining an abnormal candidate event according to the target spatio-temporal feature vector; determining the confidence of the abnormal candidate event; and determining the sensor corresponding to the abnormal candidate event of which the confidence coefficient is greater than a second threshold value as a target sensor, determining an abnormal position in the sound barrier according to a space coordinate of the target sensor, and determining an abnormal type according to a target spatial-temporal feature vector corresponding to the target sensor. By implementing the technical scheme provided by the invention, the effects of accurately identifying the abnormal condition of the sound barrier and improving the detection efficiency and accuracy are achieved.

The invention discloses a target position anomaly detection method and device, electronic equipment and a storage medium, and belongs to the technical field of video surveillance, and the method comprises the steps: periodically obtaining a video stream corresponding to a target monitoring area, and carrying out the target detection of a video frame in the video stream; if it is detected that the video frame comprises the to-be-detected target and the reference target, determining motion tracks of the to-be-detected target and the reference target in the obtained video stream; determining the relative position of the to-be-measured target and the reference target according to the motion trails of the to-be-measured target and the reference target; and performing position anomaly detection on the to-be-detected target based on the relative position and a preset anomaly detection rule corresponding to the to-be-detected target. Thus, anomaly detection is carried out through the relative positions of the multiple targets, the targets with abnormal positions can be predicted more timely and accurately, potential safety hazards are effectively prevented, and compared with an anomaly detection method of a single fixed target type in the prior art, the comprehensiveness and accuracy of a monitoring system can be improved.

The invention discloses an automobile charging gun abnormal position detection method and system and a storage medium, and the method comprises the steps: collecting the current video data of an automobile charging gun, and extracting a plurality of current state images from the current video data based on a preset period; identifying the plurality of current state images based on a preset identification model formed by training pre-collected sample data, and judging whether the automobile charging gun is in an abnormal position or not; and if the automobile charging gun is located at the abnormal position, obtaining a charging gun identifier and first position information corresponding to the automobile charging gun, and outputting abnormal prompt information based on the charging gun identifier and the first position information. According to the method, the image reflecting the real-time state of the automobile charging gun is extracted from the collected video data of the automobile charging gun, the abnormal position is recognized by the preset recognition model, and the abnormal prompt information is output, so that the automobile charging gun at the abnormal position is found in time, and the automobile charging gun is prevented from being damaged.

The application discloses a chip packaging defect detection and positioning method and device, and the method comprises the following steps: performing PCA preprocessing on a test set and a training set of a chip packaging image; training a deep tensor dictionary learning method based on the features extracted from the training set, so as to obtain a trained deep tensor dictionary; determining a sparse coding third-order matrix based on the features extracted from the test set and the trained deep tensor dictionary; and judging whether the chip packaging image to be detected has defects and positioning the defects based on the sparse coding third-order matrix. The application describes the main features of the image through a three-dimensional dictionary, strengthens the feature expression ability of the deep tensor dictionary by combining deep learning, and approximately represents the image by combining sparse coding, so that whether the image is abnormal can be judged according to the error between the actual image and the approximate representation, and the abnormal position of the image can be positioned according to the sparse coding, thereby improving the detection speed and the detection accuracy of the chip packaging defect detection.

The application relates to the technical field of data monitoring, in particular to a pipeline abnormality monitoring method and device, electronic equipment and a medium. The method comprises the following steps: comparing pressure values of each substation with corresponding preset standard pressure values of each station to determine whether an initial abnormal substation exists; if yes, determining a monitoring range corresponding to the initial abnormal substation according to an initial abnormal position; determining corresponding noise frequency and noise characteristics based on sound monitoring data corresponding to the monitoring range, and determining a first weight value based on the same; determining a predicted influence value according to the noise frequency and the noise characteristics; determining a pressure difference value by comparing the pressure value corresponding to the initial abnormal substation with the corresponding preset standard pressure value, and determining a second weight value according to the predicted influence value and the pressure difference value; determining whether the monitoring range is abnormal according to the first weight value and the second weight value, and if yes, generating warning information based on the monitoring range. The application can discover the existing abnormality of a transportation pipeline in time.

The present application belongs to the technical field of data labeling, and particularly relates to a 3D point cloud data labeling-based acceptance method and device and a storage medium. The method comprises the following steps: S1, standard presetting, in which, based on an acceptance object and an acceptance standard, a size of an element box and a fitting error of the element box are preset in advance; S2, system judgment, in which, according to a comparison between the standard presetting and actual labeling, it is judged whether the size of the element box is correct, whether the fitting of the element box is within the error range, whether the element boxes overlap, and an abnormal position and an abnormal reason are identified; and S3, inspection, in which, according to the abnormal position and the abnormal reason judged by the system, a form that can be viewed visually is used to display, and the abnormal position and the abnormal reason are clicked to inspect. The purpose is to solve the problem of misjudgment and low efficiency in the background art.

A data shaping apparatus includes: an abnormality detection unit configured to detect, as abnormal data, positioning data indicating an abnormal position from a positioning data group based on a movement constraint condition for a moving object, the positioning data group being a collection of positioning data at least indicating a position and a positioning time of the moving object; a first generation unit configured to generate first data from the positioning data group by replacing a position indicated by the abnormal data with a position assumed under a predetermined movement condition; and an output unit configured to output information based on the first data.

The invention discloses a flushing system anomaly detection method, device and system for medical equipment, and belongs to the technical field of medical equipment. The medical equipment is provided with a flushing channel, the method comprises the steps that first detection information and second detection information corresponding to the flushing channel are obtained, the first detection information represents the flushing pressure change condition of the flushing channel within the target duration, and the second detection information comprises at least one of the second detection information and fluid temperature information; the second detection information represents the bubble generation condition in the flushing channel, and the fluid temperature information represents the temperature change condition of fluid in the flushing channel; and according to the first detection information and the second detection information, abnormal alarm information corresponding to the flushing channel is output, and the abnormal alarm information indicates at least one of an abnormal position in the flushing channel and an abnormal state corresponding to the abnormal position. By means of the method, the abnormity in the flushing channel can be accurately detected.

The invention discloses a position processing model training method and device, a position processing method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: acquiring a position track sequence generated by interaction between a plurality of users and a position interaction node within a set time period; determining a target position interaction node based on all the sequences; aiming at each target position interaction node, respectively clustering all position track sequences containing the position of the node to obtain a plurality of class clusters; then, constructing a sample containing annotation information according to the centroid of each class cluster; according to the method, model training is carried out on the basis of the samples, the abnormal position processing model is generated, the training data size is reduced, noise in original data is reduced, and therefore the training accuracy and the training speed of the abnormal position processing model are effectively improved.

The invention provides a program abnormal position positioning method, a test case generation method and a related device, and relates to the field of program testing. The electronic equipment obtains a to-be-analyzed report of the test program; wherein the to-be-analyzed report comprises an abnormal screenshot of the test program and picture description information for the abnormal screenshot; determining a target screenshot most similar to the abnormal screenshot from a plurality of preset standard screenshots; wherein the plurality of standard screenshots are captured from a plurality of interfaces of the test program; determining an interface corresponding to the target screenshot as an abnormal target interface; and matching preset control structure information of the target interface with the picture description information, and determining an abnormal target control in the target interface. Thus, through combination of image matching and text analysis, efficient and accurate positioning of the abnormal position is realized, and the efficiency and quality of test report processing are significantly improved.

The application relates to the technical field of target detection, and provides an optimization method and device for target detection, which comprises the following steps: inputting a to-be-detected image into a target detection model, so that the target detection model outputs a target category, a target position and a first confidence value for determining the category; inputting the to-be-detected image into an abnormality detection model, so that the abnormality detection model outputs an abnormality position and a second confidence value for determining the abnormality; calculating the IoU of the target position and the abnormality position; if the IoU is greater than or equal to a set value, optimizing the confidence value corresponding to the target category according to the first confidence value and the second confidence value; and outputting a target detection result. The abnormality detection model and the target detection model are combined, the optimization of the confidence value corresponding to the target category in the target detection result is realized, and the simultaneous optimization of the missing and overkill of the target detection result can be realized without changing the confidence value threshold as much as possible.

The invention discloses an online optimization method and system for a special transformer. All-weather detection is carried out based on a working part; and matching a corresponding load schematic diagram according to the load parameters corresponding to the working part, determining a plurality of corresponding abnormal parts based on the load schematic diagram, and defining abnormal factors according to the plurality of abnormal parts and the corresponding abnormal positions. In the abnormal management and control logic, the part to be influenced is defined for the abnormal factors and the work function of the working part, so that targeted management and control can be carried out according to the part to be influenced, the abnormal power utilization event is clear, and whether the abnormal power utilization user is an electricity stealing user or not is judged according to the abnormal power utilization event. And meanwhile, path optimization is carried out according to a plurality of abnormal parts and the electricity stealing nodes, the power transmission path of the working part is optimized, interruption processing is carried out on the electricity stealing nodes, and online optimization is carried out on the working scene corresponding to the special transformer in time.

The present application discloses a method, system, electronic device and storage medium for monitoring the position of a sensor. The method includes obtaining the target position and position data of the sensor; obtaining the abnormal sensor corresponding to the abnormal operation data of the ship and determining the abnormal position data of the abnormal sensor; judging whether the sensor is at the target position according to the abnormal position data; if so, judging that the abnormal operation data is true; if not, adjusting the position of the abnormal sensor to the target position, deleting the abnormal operation data and then collecting the operation data of the ship again. By monitoring the operation data of the ship in real time, the accuracy of the current position of the sensor is deduced, the obtained ship navigation data is effectively utilized, and the complexity of data processing for directly monitoring the position of the sensor is reduced; further, by effectively monitoring the position of the sensor and making adaptive adjustments, the data reliability of the finally obtained ship navigation data can be ensured.

The invention discloses an abnormity identification method and device for a vehicle test tool, and relates to the technical field of test tool abnormity identification. The method comprises the following steps: in the process of testing a whole vehicle or a vehicle part through a test tool, detecting real-time relative displacement between two corresponding connecting pieces with a relative relationship by using each connecting displacement sensor; for each connection displacement sensor, calculating a reference data fluctuation threshold value corresponding to a specific position according to the historical relative displacement, detected by the connection displacement sensor in a first historical period, of the specific position of the test tool in a normal state; according to the real-time relative displacement detected by each connection displacement sensor and a reference data fluctuation threshold value, whether abnormal fluctuation exists in the real-time relative displacement or not is determined; and under the condition that any real-time relative displacement has abnormal fluctuation, a structure abnormal position is determined, and a structure abnormal alarm is given. According to the embodiment, the position with the abnormal structure in the test tool can be quickly identified and positioned.

The utility model relates to the technical field of testing fixtures, in particular to a testing fixture for an electronic control unit positioning tray, which comprises a bottom plate, a tray positioning pin, clamps, a floating plate and guide pins, the tray positioning pin, the guide pins and the clamps are mounted at the top of the bottom plate, the clamps are positioned on two sides of the top of the bottom plate, and guide holes and mounting holes are arranged on the surface of the floating plate. A slidable floating head is mounted in each mounting hole, a spring is sleeved outside each floating head, and two ends of each spring respectively abut against the lower surface of the corresponding mounting hole and a boss at the bottom of the corresponding floating head. Compared with the prior art, whether the positioning tray is deformed or not is judged through the tray positioning pin, whether the product positioning pin of the positioning tray is deformed or loosened or not can be quickly judged through whether the floating head in the floating plate mounting hole is jacked up or not, and the abnormal position can be quickly positioned; the inspection efficiency of the positioning tray is improved, and the product risk caused by the unqualified positioning tray is reduced.

The invention belongs to the field of relay protection memory self-inspection, and particularly relates to a memory exception judging and positioning method, system and device and a readable storage medium. According to the method, the row and column check values capable of representing the influence of the single event upset are obtained through the check value of the memory data and the check value between the data, whether the single event upset occurs or not can be judged only by comparing the row and column check values calculated in the current period with the row and column check values calculated in the last period, reference to memory redundant backup is not needed, and therefore extra memory is not occupied, and the data processing efficiency is improved. The utilization rate of equipment memory resources can be improved; even if the memory resources of the equipment are insufficient, the memory abnormity judgment and positioning of the single event upset are not influenced; and after the single event upset of the memory is judged, the abnormal position can be quickly and accurately positioned according to the difference between the row verification value and the column verification value of the current period and the previous period, the abnormal position is positioned without comparing the memory data and the backup data, and the positioning accuracy can be improved while the abnormal positioning efficiency is improved.

The invention relates to the technical field of test evaluation, in particular to a semi-physical simulation system test confidence coefficient online evaluation and guarantee method, which comprises the following steps of: recording asynchronous abnormal events in real time, and generating a local asynchronous event sequence which serves as basic data of subsequent confidence coefficient weakening; calculating a confidence weakening factor of each local confidence unit, reflecting the real influence degree of the asynchronous abnormal event in the local region, and outputting a local confidence evaluation result; according to the method, the overall test credibility is subjected to decreasing type recombination, an overall credibility structure capable of indicating a local abnormal position is formed, the situation that the test is misjudged to pass when asynchronous abnormity exists is avoided, and abnormal positioning information for backtracking and an independent local confidence record are generated. According to the method, the structural decline mode not only improves the response capability of the overall assessment to the chain propagation risk, but also realizes the conversion of credibility calculation from static weighting to dynamic conduction.

The invention provides an abnormal detection positioning method and system for an electromagnetic test, and the method comprises the steps: carrying out the electromagnetic interference test of a target device, and obtaining abnormal position information and abnormal test parameters when a detection abnormality occurs; obtaining an equipment abnormal area and a plurality of implicit abnormal elements in the equipment abnormal area according to the abnormal position information and an index, and collecting an infrared image of the equipment abnormal area; according to the plurality of implicit abnormal elements, carrying out integration level analysis to obtain an integration level, and configuring a detection positioning coefficient; and according to the infrared image, identifying a plurality of temperature anomalies of the plurality of implicit abnormal elements, respectively correcting the detection positioning coefficients, and respectively carrying out array anomaly identification according to the anomaly test parameters to obtain a plurality of element anomaly probabilities as anomaly detection positioning results. The technical problem that in the prior art, when electronic equipment is subjected to electromagnetic interference testing, abnormal element detection and positioning are not accurate is solved.