103 results about "Inspection time" patented technology

The invention provides an intelligent substation inspection method and system based on the Internet of Things, and the method comprises the steps: collecting a multi-mode perception data set, and constructing a real-time three-dimensional map of a substation; constructing a cross-modal generation type fault detection model, and extracting fusion features to obtain a diagnosis result; formulating an optimal inspection path and plan by using a double-layer reinforcement learning framework and an evolutionary algorithm; and executing inspection based on the map to obtain a result. According to the method, multi-source equipment data are fused through the bionic sensing network, and the complex fault recognition capability is improved in combination with the generative fault detection model; the real-time three-dimensional map is based on laser radar SLAM and UWB fusion positioning, and is dynamically updated by using an ICP algorithm, so that the static map problem is solved, and the collision risk is reduced; double-layer reinforcement learning and an evolutionary algorithm are combined to realize multi-objective collaborative optimization, so that high-efficiency routing inspection is guaranteed, energy consumption is reduced, and time is shortened; and a closed loop verification mechanism dynamically adjusts the strategy, so that the adaptive capacity of the system is enhanced, and the inspection accuracy, efficiency and flexibility are improved.

The invention provides a three-coordinate detection and evaluation method for a mortise of a fir-tree-shaped turbine disc of a gas turbine, and the method comprises the following steps: S1, placing the turbine disc on a three-coordinate detection platform, and keeping the position of a first groove of the turbine disc in the Y-axis direction of a three-coordinate machine tool; s2, importing a digital model, acquiring two points of two opposite working surfaces of a wheel disc reference surface, a reference circle and a first groove by a three-coordinate probe according to drawing requirements, and establishing a coordinate system; s3, deconstructing each size element required to be measured for the mortise; and S4, collecting geometrical characteristics in the mortise in a three-coordinate manner, constructing corresponding size elements for evaluation, judging whether the requirements of a drawing are met or not according to a preset tolerance threshold value, and completing detection. The method can be suitable for various types of gas compressor dovetail type mortises and turbine fir tree type mortises of other sizes, a complex inspection method of an auxiliary measuring tool is converted into a simple, efficient and good-universality geometric feature structure detection method, manual detection time and research and development design cost are saved, and accuracy is improved.

The invention belongs to the technical field of locomotive equipment maintenance, and provides an automatic inspection system and method for a locomotive cooling tower, and the method comprises the steps: obtaining the basic information of the locomotive cooling tower, planning an inspection path for an inspection machine, controlling the inspection machine to inspect along the inspection path, and collecting the visual information of the locomotive cooling tower, the method comprises the following steps: constructing a local three-dimensional grid map in real time, detecting obstacles, evaluating a collision risk between the obstacles and an inspection machine, if the collision risk is high, triggering the inspection machine to avoid obstacles, constructing a global three-dimensional grid map when the inspection machine completes an inspection path, and determining an inspection blind area under the global three-dimensional grid map. And judging whether the current inspection time period is ended or not, if not, triggering supplementary inspection, otherwise, planning an inspection path for the next inspection time period, and after the inspection time period is ended, performing anomaly detection and positioning on each part of the locomotive cooling tower based on visual information acquired by an inspection machine through a machine learning algorithm.

The invention discloses an unmanned aerial vehicle inspection system and method for an intelligent street lamp based on multi-source video fusion, and relates to the technical field of unmanned aerial vehicle inspection, and the system comprises an inspection data collection module, an inspection image analysis module, an inspection mode switching module and an inspection task dynamic evaluation module. The inspection data acquisition module, the inspection image analysis module, the inspection mode switching module and the inspection task dynamic evaluation module are in communication connection with one another; wherein the inspection data acquisition module is used for performing flight inspection on street lamps in a target inspection area one by one through an unmanned aerial vehicle with a visible light camera, and setting image calibration array points to match the accuracy of inspection image acquisition; the inspection image analysis module is used for processing and analyzing the acquired image data; according to the invention, the problem that the inspection time consumption and the data processing burden are greatly increased in the existing multi-source image fusion processing mode is solved.

Various surface and structural defects are currently inspected visually. This method is labor intensive, requiring large maintenance man hours, and is prone to errors. To streamline this process, herein is described an automated inspection system and apparatus based on several optical technologies that drastically reduces inspection time, provides accurate detection of defects, and provides a digital map of the location of defects. The technology uses a sensor that includes a plurality of light sources for emitting light on the structural surface, and a camera for detecting a shadow or an image shift of the structural surface feature. Furthermore, the technology utilizes an image processing and correction apparatus for performing a pattern image and structural surface defect map detection and generate a distortion corrected defect map for a surface scan area on the structure that is incident on the sensor.

The present invention discloses a parallel computing method and system for vector space data quality inspection, comprising the following steps: obtaining vector space data, and inspecting the vector space data according to a quality inspection scheme; extracting four types of quality inspection items from the vector space data that has passed the inspection according to a quality inspection item classification method to obtain four quality inspection item sets, and further decomposing the four quality inspection item sets to obtain a number of quality inspection sub-items corresponding to the four quality inspection item sets; assigning quality inspection tasks to the several quality inspection sub-items according to the fixed time step of the execution of a single quality inspection task and the type of quality inspection item set to generate quality inspection tasks; performing parallel quality inspection according to the assigned quality inspection tasks until all quality inspection tasks are completed. The beneficial effects of the present invention are: the time of quality inspection is greatly shortened under the premise of ensuring the correctness of the quality inspection results, and the efficiency of vector space data quality inspection is improved.

The invention belongs to the technical field of distributed energy storage information monitoring, and particularly relates to a distributed energy storage information abnormal data real-time monitoring and early warning method, which comprises the following steps of: constructing an information database according to energy storage equipment of each energy storage site, and formulating an inspection route by utilizing a genetic algorithm; according to the use frequency, the historical working state and the importance degree of the energy storage equipment, formulating the grade of the energy storage equipment, and formulating an inspection time preset threshold value of the energy storage equipment of each grade; obtaining a normal coefficient according to the state score and the safety value; and obtaining equipment grade change conditions, and carrying out alarm and inspection judgment. According to the method, the use frequency, the historical working state and the importance degree of the energy storage equipment are comprehensively evaluated to customize the equipment level, the safety value of the equipment is determined by combining the inspection time and the inspection frequency of the inspection personnel on the equipment, and the inspection behavior reliability of the energy storage equipment is comprehensively evaluated.

The invention relates to the technical field of intelligent construction sites, in particular to an intelligent inspection method, device and system for an intelligent construction site based on a space-time diagram convolutional network, and a storage medium, and the method comprises the steps: installing video collection equipment at a construction site, and carrying out the image data collection of construction activities, equipment and stockpile of each region of the construction site; modeling the constructor behavior prediction model, and predicting the flow trend and density of constructors in each region by integrating the construction site construction layout and construction information; processing the acquired image information of each construction area by using an image recognition technology, and recognizing the type, density and related distribution position of equipment; the construction site environment information is fused, the estimated inspection time of each construction area is evaluated, and the relation between the construction equipment density and the inspection time is analyzed; and determining the guarantee range of the spare part maintenance points according to the analysis result of the construction equipment, and adjusting and optimizing the number distribution of the spare part maintenance points according to the utilization efficiency of the existing spare part maintenance points, the flow of construction personnel and the area of the construction region. A reinforcement learning technology is used to optimize inspection roads of inspection personnel, and based on construction personnel flow trend prediction and construction equipment quantity analysis, routing inspection is arranged preferentially in a route of high-utilization-rate equipment and a time period with less construction operation, so that fault hidden dangers are found in time, and the influence on normal construction operation is reduced; and the execution condition of the inspection task is monitored in real time, and the inspection efficiency and the fault risk early warning level are evaluated in combination with the condition fed back by the inspection personnel.

The invention discloses an electric power intelligent inspection method and system, relates to the technical field of electric power inspection, and solves the technical problems that the inspection route is unreasonable and the inspection time and cost are increased due to the lack of a scientific and systematic method when the inspection route is planned. According to the method, the fault record in the historical inspection data is analyzed in detail, the equipment abnormal value of the inspection equipment is calculated, the risk equipment and the normal equipment are classified according to the preset comparison value, the potential risk equipment can be identified more accurately based on the data driving method, maintenance measures can be taken in advance, and the maintenance efficiency is improved. The routing inspection starting point, the routing inspection terminal point and the distribution condition of the risk equipment are comprehensively considered, the shortest routing inspection path is determined firstly, then insertion analysis is performed on the risk equipment not on the target path, the path optimization sequence is reasonably arranged according to the number of the risk equipment on the two sides of the path point, the total length of the routing inspection path can be reduced, and the routing inspection efficiency is improved. And meanwhile, the risk equipment can be inspected in time.

The invention provides a partial discharge defect position identification method based on an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicles, and the method specifically comprises the steps: carrying out the determination of the inspection time of the unit length of different inspection intervals according to the distribution condition of suspected partial discharge abnormal points in the inspection intervals, obtaining the weather data of an inspection plan time period, and obtaining the weather data of the inspection plan time period; the identification accuracy of the partial discharge defect position is determined by combining the transmission power and the voltage grade of the power transmission line, and when the identification accuracy meets the requirement, the inspection time per unit length and the partial discharge detection height are taken as the reference, the ultraviolet camera of the unmanned aerial vehicle is utilized to carry out partial discharge inspection processing in the inspection interval to obtain an ultraviolet image; and the partial discharge defect position is determined according to the analysis result of the ultraviolet image and the evaluation result of the consistency of the ultraviolet image and the visual image, so that the recognition processing accuracy of the partial discharge defect position is ensured.

The invention relates to the field of data processing, in particular to an intelligent routing inspection management method and system, when a routing inspection user performs routing inspection, reference information constructed in advance is provided for routing inspection personnel for reference, so that the routing inspection personnel can know the reference range and routing inspection time of each routing inspection point location of each routing inspection item. And the inspection personnel unfamiliar with the project can be helped to quickly know the inspection project. Besides, the priority of each inspection point location is determined by utilizing environmental parameters acquired by a sensor arranged on a construction site project in real time and real-time positioning of the inspection personnel, and when the inspection personnel execute an inspection task, the priority and reference information are utilized to guide and supervise the inspection personnel. The inspection process of the inspector can be effectively guided, supervised and managed, and the inspector is helped to improve the inspection efficiency and the inspection quality.

Disclosed is an IoT system for smart gas inspection supervision and distribution, comprising: a public user platform, a citizen cloud service platform, a government gas supervision management platform, a government gas supervision sensor network platform, a government gas supervision object platform, a gas company sensor network platform, and a gas equipment object platform. The government gas supervision management platform is configured to obtain an inspection route; determine a plurality of inspection sub-routes; generate a calling instruction; generate an acquisition instruction and send the acquisition instruction to the gas equipment object platform; determine an inspection type parameter of the inspection sub-routes; divide the inspection sub-routes into three categories of objects to be inspected, and determine candidate assignment parameters of the three categories of objects to be inspected; determine predicted inspection time; determine an inspection assignment parameter; generate an inspection instruction and send the inspection instruction to the gas equipment object platform.

The invention provides a machine room inspection quality evaluation method, device and equipment and a storage medium, and the method comprises the steps: recording the inspection time consumption of an inspector in each cabinet, determining the inspection timeout rate according to the inspection time consumption, detecting the abnormal behavior information under the condition that the cabinet door of the cabinet is opened, and determining the inspection timeout rate according to the inspection timeout rate. Determining an abnormal behavior rate according to the abnormal behavior information, determining fault information of the cabinet after inspection is finished, determining an equipment fault rate according to the fault information, and determining an inspection quality score according to an inspection timeout rate, the abnormal behavior rate and the equipment fault rate; the inspection timeout rate, the abnormal behavior rate and the equipment failure rate are automatically determined, the inspection quality score is calculated, the machine room inspection quality evaluation can be effectively carried out, and the inspection efficiency and level of machine room inspection personnel can be supervised and improved.

The embodiment of the invention provides a tower cylinder bolt abnormity identification method and device. The method comprises the steps that an image acquisition device arranged on a tower cylinder inspection robot is used for acquiring a front image and a side image of each tower cylinder bolt; performing preprocessing operation on the front image and the side image to obtain a front image block corresponding to the front image and a side image block corresponding to the side image; performing abnormal region identification on the front image block and thread feature identification on the side image block respectively, and outputting identification results including an abnormal type, a quantization parameter and confidence respectively; performing grading processing on the recognition result according to the confidence coefficient in the recognition result, and outputting a judgment result; and the judgment result, the bolt ID and the inspection time are stored in an associated mode, and alarm information is output according to the judgment result and pushed to a manager terminal device. According to the invention, the safety and the detection efficiency of tower bolt inspection can be improved.

The invention discloses an intelligent inspection method for concrete appearance quality, and relates to the technical field of concrete appearance quality detection.The method comprises the following steps that the optimal inspection height is configured for intelligent inspection of an unmanned aerial vehicle based on the detection precision requirement and the detection efficiency requirement of voids and pitted surfaces; the unmanned aerial vehicle inspects the surface of the concrete structure according to the preset route and the set optimal inspection height, and the airborne high-resolution imaging system is used for continuously shooting the detection area. Through a dynamic inspection mechanism based on confidence score driving, the unmanned aerial vehicle can actively and adaptively adjust the inspection height and the inspection time when finding a suspected slight pitted surface, the image resolution and the defect feature detail expression capability are improved, the recognition capability of the convolutional neural network on the slight pitted surface and a dark honeycomb is enhanced, and the detection accuracy is improved. The defect omission ratio and the false detection rate in the inspection process are effectively reduced, and accurate, stable and efficient recognition of hidden defects such as slight pitted surfaces and deep honeycombs on the surface of the concrete structure is achieved.

This invention discloses a material inspection and monitoring method and device based on UWB positioning and multi-view linkage. The method includes: acquiring the sample number of the target material, wherein the target material is equipped with a UWB tag, and the UWB tag and the sample number correspond one-to-one; collecting the inspection time of the target material entering each inspection area according to the UWB tag, and constructing a time-location correlation matrix; and combining the time-location correlation matrix with a preset multi-view relationship model with the inspection area to form multi-view monitoring content, thereby completing the monitoring of the target material inspection process. By constructing a time-location correlation matrix and piecing together multi-view monitoring content, the monitoring of the target material inspection process is completed, providing a basis for rapid and accurate traceability of the target material inspection process afterward, while improving the level of material quality supervision and control, promoting quality inspection efficiency and effectiveness, and realizing full-process tracking and monitoring of the material inspection process.

The present application relates to a multi-UAV trajectory planning method and device applicable to large-area power transmission lines, the method comprising obtaining inspection area information and inspection task information; constructing a transmission line topology map of the area to be inspected based on the inspection area information and the inspection task information, dividing the transmission line topology map into inspection route units and assigning UAVs based on the path optimization model; planning the first flight trajectory of each UAV based on the transmission tower coordinates, cable length and parking point coordinates in a single inspection route unit; judging the type of the current inspection target based on the image collected by the UAV and executing the corresponding inspection strategy respectively. By dividing the transmission line into several inspection route units, the method enables multiple UAVs to perform inspections and take photos in parallel, which can efficiently and quickly complete their respective inspection tasks, save inspection time, and effectively improve inspection efficiency. At the same time, the flight trajectory is corrected for different inspection route units in a targeted manner to achieve refined inspections and improve the quality of line inspections.

The invention discloses a transformer substation and converter station equipment intelligent inspection method and system and terminal equipment, and belongs to the technical field of power system operation management. According to the method, equipment information of power transmission, power transformation and power distribution links is integrated by constructing a power transmission, power transformation and power distribution combined inspection model, and an intelligent terminal cluster is cooperatively scheduled to execute a cross-link combined inspection task; collecting visible light images, infrared thermal imaging data and operation state data of the equipment according to preset point positions, performing deep learning on the collected data, automatically identifying abnormal conditions including equipment appearance damage, meter reading deviation, internal and external heating and discharging of the equipment and foreign matter invasion, and obtaining inspection data; statistical analysis is carried out on the inspection data, a visual report containing inspection time, equipment states and defect information is generated, and historical data tracing and multi-source data association analysis are supported. Automatic and efficient inspection and intelligent identification analysis of the equipment are realized, the inspection efficiency and accuracy are improved, and the method is suitable for operation and maintenance management of large-scale complex power grid equipment.

The utility model belongs to the technical field of specimen racks, and particularly relates to a specimen rack capable of reminding regularly, which comprises a specimen rack, a cover body, a mounting seat, a hasp and a buckle seat are arranged on the specimen rack, a refrigeration box is inserted in the specimen rack, a suction plate and a timer are arranged in the mounting seat, and the hasp and the buckle seat are arranged on the specimen rack. A plurality of specimen grooves are formed in the specimen frame, rubber mats and bottom plates are arranged in the specimen grooves, and air holes are formed in the bottom plates. The specimen frame has a timing function, so that the inspection time can be conveniently set, and after the set time is reached, the specimen frame can be conveniently taken out by a user. And the medical staff is prompted, so that the phenomenon that the medical staff forgets to send and misses sending due to the fact that the medical staff cannot pay attention to time all the time under busy work, and then specimen detection is affected is avoided.

The invention relates to the Internet of Things video analysis technology, in particular to an intelligent construction site remote monitoring system based on the Internet of Things, which is characterized in that firstly, a risk point location analysis module is combined with historical fault data to intelligently analyze the risk index of each region, and then a data acquisition module is used for collecting personnel information and video monitoring data; the video analysis module analyzes the video monitoring data and records the troubleshooting time of the staff in each region, and the evaluation accuracy analysis module adjusts the corresponding standard troubleshooting time in combination with the regional risk index. And the evaluation accuracy of the working personnel on the target area is calculated by considering the influence of the working experience of the working personnel on the troubleshooting accuracy, so that a more objective and accurate evaluation result is obtained. The warning module can give an alarm in time when it is found that the evaluation accuracy is insufficient according to a set accuracy threshold value, so that correction measures are taken in time when the evaluation accuracy of workers is insufficient, and the safety management level of the construction site is greatly improved.

The present application provides an inspection method and system integrating three inspection units: stretching, reverse bending, and weight deviation, including: stretching the first target to be inspected according to the inspection scheme, and while the first target to be inspected is being stretched, measuring the length, weighing, and bending the second target to be inspected to obtain the inspection data of the second target, wherein the first target and the second target belong to the same batch of samples to be inspected; after the first target to be inspected is stretched, the inspection data of the first target is obtained; the inspection data of the first target and the inspection data of the second target are imported into a trained recognition model to obtain the inspection result of the target to be inspected. By completing other inspections of the second target to be inspected while the first target to be inspected is being stretched, the inspection items are changed from the original series form to a parallel and simultaneous mode, thereby shortening the overall inspection time.

The utility model provides an inspection well elevation inspection device which comprises a ruler and two movable clamping bases, the two movable clamping bases clamp the two ends of the ruler respectively, and the two ends of the ruler can move up and down in the movable clamping bases. The beneficial effects of the utility model are that through presetting the height of the horizontal ruler, the detection of the construction elevation of the inspection well can be rapidly completed, whether the construction qualified condition is satisfied or not can be timely confirmed, the detection time after construction is greatly shortened, the project progress is improved, and all measurement processes can be completed by one person. The device is light in structural design and convenient to carry and move, so that the measurement process is more flexible and efficient, and the labor cost and the time cost are greatly reduced.

The invention discloses a knowledge recheck method, equipment, a storage medium and a device. Task execution parameters are configured based on an inspection mode corresponding to a knowledge reinspection task; calling a target inspection tool to execute inspection based on slice information and task execution parameters in a preset knowledge base, and obtaining an execution record; and performing problem positioning and slice tracing on the execution records to obtain knowledge optimization suggestions. Compared with the situation that real-time synchronization of knowledge and a valid system cannot be guaranteed through manual auditing, and the auditing efficiency is low, the method calls an inspection tool to execute inspection through slicing information and task execution parameters configured in a user-defined mode to achieve modular extraction and slicing of self-adaptive structured knowledge content, the data integrity is greatly improved, and the efficiency of the self-adaptive structured knowledge is improved. The inspection time is greatly shortened, and the inspection efficiency is improved.

The invention relates to the technical field of air conditioner appearance intelligent detection data management, and particularly discloses an air conditioner appearance intelligent detection data management platform based on AI vision, and the platform comprises a position restoration evaluation module, a code reader code scanning module, an air conditioner appearance quality inspection module, a database, and a detection precision evaluation module. According to the invention, air conditioner products whose positions are not corrected are corrected through the air valve body in the quality inspection equipment, whether the appearance quality inspection of the air conditioner products is qualified is assessed according to the quality inspection data corresponding to each component in each detection group in each detection surface of the air conditioner products, and finally, the air conditioner products whose appearance quality inspection is qualified are subjected to manual spot inspection, so that the quality inspection efficiency of the air conditioner products is improved. And the detection work accuracy condition of the detection post is deeply analyzed according to the manual spot check data, so that the quality inspection complexity is reduced, the detection time is shortened, the timeliness of finding errors or omissions in the quality inspection process is improved, and an enterprise is helped to accurately know the actual effect and existing problems of the quality inspection link.

The utility model discloses a sample collection device for a clinical laboratory. The sample collection device comprises a shaking mechanism and a fixing mechanism, the shaking mechanism comprises a base, a motor is fixedly mounted at the top of the base through a vertical plate, a rack is movably connected to the top of the base, a connecting plate is fixedly connected to one side of the rack, an empty groove is formed in the connecting plate, and a sliding block is slidably connected to the inner side wall of the empty groove; according to the sample collection device for the clinical laboratory, through the arrangement of the shaking mechanism and the positioning mechanism, a threaded rod can drive a movable block to approach a fixed block after being tightened, so that the two ends of a glass tube filled with a sample can be positioned, and after a motor is started, a rack can drive a rotating shaft to rotate back and forth through a gear; further, the sample can be in a shaken state all the time; through the arrangement, anticoagulation of a sample in a manual shaking mode is avoided, so that the working efficiency during inspection is improved, and the inspection time of a patient is shortened.

The invention discloses an intelligent inspection management system based on a face recognition technology, relates to the technical field of intelligent management, and solves the technical problem that in the prior art, the situation that an inspector possibly replaces a post in the inspection process is not considered, and the inspector is difficult to manage. The method comprises the following steps: acquiring facial feature information of inspection personnel through a face recognition technology, and constructing an identity verification library of the inspection personnel; acquiring inspection time, and scheduling inspection personnel according to the inspection time; managing attendance information of the inspection personnel according to the scheduling information; the identity of the inspector is verified before the inspector performs inspection work; the rating of the inspection personnel is adjusted regularly; acquiring an abnormal image uploaded by the inspection personnel, and analyzing according to the abnormal image to obtain an abnormal reason; and the abnormity is processed according to the abnormity reason, and the processing result is fed back to the management personnel, so that the identity of the inspection personnel can be verified, and the condition of post replacement or post lack is avoided.

The present invention relates to the technical field of fabric surface defect detection, and specifically discloses a machine vision-based fabric surface defect detection method and system. The method comprises analyzing the pilling characteristics of a fabric surface image to obtain the ratio of the number of abnormal areas, judging the fabric quality, and generating an unqualified signal and a suspected qualified signal; analyzing the abnormal marking results of the fabric surface areas to obtain a pilling trend value, determining the impact of fabric surface pilling on fabric surface defects, and generating a strong impact signal; and extracting all fabric samples with unqualified signals and strong impact signals within a test time, recording them as unqualified traceability samples and strong impact traceability samples, respectively. The present invention optimizes the fabric inspection time by determining the fabric optimal inspection time and the inspection time of fabric production equipment, and analyzing the fabric production inspection time. This not only improves the pertinence and efficiency of the inspection, but also helps to promptly discover and resolve problems in the production process, thereby ensuring the quality of the fabric.

The invention discloses an inspection control method based on a robot with a body and a related device. The method comprises the following steps: determining inspection boundaries of an open area and a non-open area; performing path planning on the plurality of inspection points in the to-be-inspected area according to the inspection boundary to obtain navigation information; if the robot with the body detects a first obstacle when moving according to the navigation information, executing a passing skill operation corresponding to an entering mode of the first obstacle in a skill library; determining the maintenance time of the passing state according to the leaving mode of the first obstacle and the inspection time demand; when it is detected that the robot with the body arrives at the target inspection point, activating a carried inspection device for inspection based on the inspection index of the target inspection point to obtain a first inspection result; performing visual analysis on the obtained inspection object image in the target inspection point to obtain a second inspection result; and generating an inspection report according to the first inspection result and the second inspection result. According to the invention, the inspection efficiency and the intelligent level of inspection are improved.

The present invention belongs to the technical field of electronic component reliability assessment, and discloses a component reliability assessment method and device, the method comprising: obtaining historical inspection data and production process parameters, reliability expectation values ​​and production process parameters of a batch to be assessed; statistically analyzing the historical inspection data to establish a product reliability baseline and failure rate statistical model parameters; using the baseline and expected values ​​to perform statistical estimation to obtain a production sample plan and a sampling plan; using the baseline after the associated parameters, the production process parameters of the batch to be assessed, the model parameters after the associated indicators and expected values ​​to perform statistical estimation and comparison, and determining a screening method and an inspection method; using a screening plan composed of a production sample plan and a screening method to assess screening reliability, obtain screening reliability data, and determine an inspection plan after comparing it with the product reliability baseline. The method uses historical data for component reliability assessment, improves data utilization, and reduces inspection time and expenses.

The invention discloses a metro vehicle bottom fastener looseness detection method based on target detection and image processing, which comprises the following steps: firstly, acquiring an image of a metro vehicle bottom fastener area, constructing a training set and a test set based on the image, constructing an improved YOLOv11 target detection model, training the model by using the training set, and detecting the looseness of the metro vehicle bottom fastener area. The method comprises the steps of training a target detection model, detecting fasteners in a fastener area of a subway vehicle bottom based on the trained target detection model, cutting to obtain a single fastener image, and finally detecting the extracted single fastener image to judge whether the fasteners are loosened or not. Compared with a traditional manual inspection mode, the scheme has the advantages that the inspection time is greatly shortened through a target detection technology and an image processing method, more vehicle bottom areas can be covered in shorter time, large-scale and frequent detection requirements are met, the influence of human factors can be eliminated, the stability and consistency of detection are improved, and the detection efficiency is improved. And the detection result is more reliable.