912 results about "Inspection method" patented technology

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The invention relates to the technical field of fruit and peach screening, in particular to a fruit and peach multi-stage screening and quality detection method which comprises the following steps: S1, fruit and peach variety judgment: collecting multi-angle images, extracting color, texture and contour features, and outputting variety labels and parameter sets in a classified manner; s2, growth feature mapping: extracting structure key points, matching the structure key points with a variety template, and generating a growth deviation vector; s3, area perception analysis: dividing fruit surface areas based on growth deviation, and independently identifying defects and maturity; s4, grade discrimination: fusing identification results, combining varieties and deviations, and dynamically evaluating a comprehensive quality label; and S5, screening instruction generation: converting the quality label into a control instruction, and driving the sorting device to complete output. According to the invention, integrated automatic processing of intelligent identification of fruit and peach varieties, accurate detection of regional defects and grade screening control is realized, and the accuracy and flexibility of fruit and peach screening are significantly improved.

The invention belongs to the technical field of intelligent robots and environment perception, and particularly relates to a multi-mode autonomous positioning and inspection method and system for a hazardous chemical substance warehouse environment. According to the method, robust continuous positioning in a high-pollution characteristic environment is realized through combination of a layered confidence scheduling mechanism, an observation judgment mechanism and a time sequence clustering compensation algorithm; through path local reconstruction based on task semantics, the inspection system has task understanding and autonomous adjustment capabilities and adapts to multiple types of inspection tasks; the autonomous sustainable operation capability of the system in continuous inspection is improved through energy consumption and risk double-constraint control; a multi-mode degradation identification and self-healing mechanism ensures long-term stable operation of the sensor, and manual intervention is reduced; the whole inspection system can realize all-weather and unattended multi-mode autonomous inspection in a dangerous chemical warehouse scene, and provides stable mobile platform support for subsequent dangerous symptom identification and automatic early warning.

The invention relates to the technical field of automatic operation and maintenance of a power system, in particular to an automatic operation and maintenance method and system for plant-station equipment based on a knowledge graph, and the method comprises the steps: collecting static ledger data, real-time operation data and historical operation and maintenance data of the plant-station equipment, constructing a multi-dimensional association knowledge graph, creating a digital twinborn body for target equipment in a plant station, and carrying out the operation and maintenance of the plant-station equipment. Establishing a bidirectional data channel between the digital twin and a corresponding equipment entity node in the knowledge graph; and based on a state evaluation rule and a fault propagation path model preset in the knowledge graph, performing real-time health state evaluation on the target equipment by using the state evaluation rule and the fault propagation path model to generate a health index, and when an abnormality is monitored, tracing along the topological relation between the electrical connection and the equipment in the knowledge graph, the fault source is positioned, the confidence probability is calculated, risk level evaluation and processing priority ranking are carried out according to the confidence probability, an operation and maintenance diagnosis work order containing an execution time sequence is obtained, and the fault reason can be quickly traced and positioned.

The invention provides an automatic microbial sample inspection device and method, the device comprises a base, a support column vertically arranged on the base, a sliding sleeve slidably arranged on the support column and a first driving assembly used for driving the sliding sleeve to lift up and down, a rotating seat is rotatably arranged on the sliding sleeve, and a second driving assembly is arranged between the rotating seat and the sliding sleeve. A plurality of needle tubes which are uniformly distributed in the circumferential direction of the supporting column and are used for containing reagents are arranged on the rotating seat, a sample bottle which is vertically arranged on the base is arranged below one needle tube, a detection head is arranged below the sample bottle, and the detection head is connected with a control circuit board; a third driving assembly used for pushing a piston in a needle tube located over the sample bottle is further arranged on the supporting column, and the first driving assembly, the second driving assembly and the third driving assembly are connected with the control circuit board. Due to the fact that the needle tubes are arranged, and the reagents contained in the needle tubes are different, the multiple reagents can be injected into the sample bottles through the device respectively, and diversity of inspection functions is achieved.

The invention discloses an automobile casting multi-equipment detection data fusion judgment method, relates to the technical field of intelligent detection, and solves the problems that in an existing detection method, confidence output of multi-modal data is incomparable, misinformation is easily caused by evidence conflicts, and positioning offset is generated due to the fact that multi-modal registration is limited by a rigid model. According to the technical scheme, the method comprises the steps that surface images, point cloud and X-ray projection data are preprocessed, a detection response matrix is established, probability mapping is conducted on each modal score to form likelihood distribution and uncertainty representation, weighted fusion is completed in combination with a redundancy coefficient matrix, and a conflict judgment mark is generated; on the basis, hierarchical registration is executed, and a final judgment and recheck label is output; based on the technical scheme, the judgment accuracy and stability of multi-source detection data fusion of the automobile casting are remarkably improved.

The invention discloses a fixed source carbon emission data quality comprehensive evaluation method and system, and relates to the technical field of carbon emission monitoring, and the method comprises the steps: S1, collecting dual-source monitoring and production data, carrying out the invalid recognition, emission amount accounting aggregation, working condition classification and correlation coefficient construction, and building a score working condition basic data set through variable coefficient detection; s2, calculating a correlation coefficient of the to-be-inspected valid data, and obtaining a valid data risk comprehensive score through multi-dimensional inspection scoring and in combination with an industry weight; s3, calculating an invalid data proportion as an invalid data risk score; and S4, performing two-dimensional risk rating on the valid data and the invalid data, and outputting a four-dimensional result. The system realizes the method through four layers of modules of data input, processing, risk evaluation and result output, and solves the technical problems that the existing data quality evaluation only pays attention to valid data, the inspection method is single, and a complete dual-source collaborative quality control and full-dimensional quality control scheme is lacked.

The invention relates to a power equipment inspection method, device and equipment, a readable storage medium and a product, and relates to the technical field of power. Comprising the following steps: acquiring equipment image data, equipment temperature data and equipment type of to-be-inspected power equipment; performing external health analysis on the to-be-inspected power equipment based on the equipment image data to obtain external health state information of the to-be-inspected power equipment; performing internal health analysis on the to-be-inspected power equipment based on the equipment temperature data to obtain internal health state information of the to-be-inspected power equipment; performing priority analysis on the to-be-inspected power equipment based on the equipment type to obtain an inspection priority of the to-be-inspected power equipment; according to the external health state information, the internal health state information and the inspection priority, determining an inspection strategy of the to-be-inspected power equipment; and controlling the inspection robot to inspect the to-be-inspected power equipment according to the inspection strategy. The method can improve the inspection reliability of the power equipment.

An unmanned aerial vehicle autonomous electric power facility inspection method based on three-dimensional semantic driving relates to the technical field of unmanned aerial vehicle cruise control, and comprises the following steps: constructing a three-dimensional semantic representation containing key components under a unified world coordinate system, and generating a three-dimensional semantic point cloud with component category labels in real time in combination with airborne visual perception; and the inspection requirements in the operation and maintenance regulation are analyzed into an ordered component-level task sequence, and on the basis, the unmanned aerial vehicle autonomously plans an observation pose and a local flight path according to the three-dimensional semantic distribution of the target component, so that the adaptive approaching and coverage of the key component are realized. According to the invention, operation and maintenance semantics can be precisely followed, fine-grained sensing of key parts is realized, an inspection path is enabled to be close to a concern point, environment adaptability is realized, the inspection efficiency is effectively improved, the cost is reduced, and the problems that current electric power facility unmanned aerial vehicle inspection depends on a preset mode, the operation and maintenance semantics are difficult to meet, and key part sensing and adaptability are lacked are solved. And the inspection tour effect is not good enough.

According to the invention, an unmanned aerial vehicle platform is creatively combined with the improved YOLOv11, and the unmanned aerial vehicle construction site safety helmet intelligent inspection method based on the improved YOLOv11 is provided. The method comprises the following steps: firstly, carrying out digital planning on an inspection route, and generating an executable flight path file; the unmanned aerial vehicle carries high-definition camera equipment, and carries out omnibearing video acquisition on a construction area according to a preset route; and the ground control center receives the real-time video stream data and inputs the real-time video stream data into a specially-designed safety helmet identification model to complete intelligent analysis of the state of personnel safety equipment and record of violation events. The detection model is subjected to triple optimization on the basis of a YOLOv11 architecture: a C3k2SPD module is adopted to replace a C3k2 module in an original backbone network, an ASPP module is introduced to replace an original SPPF module, and meanwhile, an NWD loss function is fused at a detection head part, so that the small target recognition capability and the complex scene fitness are remarkably improved.

The invention provides a port equipment intelligent inspection method based on multi-source data, and the method comprises the steps: enabling an unmanned plane to synchronously collect the multi-modal data, such as infrared thermal imaging, three-axis vibration, visible light and time sequence working conditions, and achieving the heterogeneous data alignment through high-precision space-time marking; performing normalization correction, filtering transformation and enhancement processing on each modal data to form a standard feature tensor; constructing a causal sensitivity initialization weight in combination with a historical fault sample, driving a dynamic weighted fusion network to generate multi-modal joint representation, and outputting an abnormal score and structured attribution explanation in a double-path reasoning network; through user feedback and man-machine collaborative correction, weight closed-loop adaptive evolution is realized, the accuracy and discrimination interpretability of equipment anomaly detection are improved, and maintenance decision and continuous optimization of an inspection system are facilitated.

The invention discloses a substation inspection method and equipment based on an inspection robot and a storage medium, and the method comprises the steps: collecting environment data through an environment sensing module after the robot is started, carrying out the fusion processing through a positioning and calculation module, and constructing a substation high-precision semantic map containing the equipment position and obstacle information; based on the semantic map and a preset inspection task, adopting an improved ant colony-deep reinforcement learning algorithm to plan an optimal path covering all inspection target points, and supporting dynamic optimization according to environmental changes; according to the planned path, high-precision navigation is realized through a multi-source fusion positioning scheme, the mobile execution module is controlled to move to each inspection point, and a data acquisition task is executed; in the cruising process, front obstacles are sensed in real time, and a layered obstacle avoidance strategy is executed according to the types and danger levels of the obstacles; and when all inspection tasks are completed or the electric quantity is lower than a threshold value, automatically planning a path and returning to the charging base station for charging.

A method for inspecting an erroneous / missing part of a discharge valve structure according to the present invention is a method for inspecting the erroneous / missing part of the discharge valve structure (1) of a compressor. The discharge valve structure (1) has an assembly structure including a partition wall (12), a discharge valve (12), a valve retainer (300) and a fixing bolt (40), and an inspection structure in which the valve retainer has an inspection hole (33) for inserting an inspection member and at least a part or the whole of the discharge valve enters a region of the discharge valve (20) corresponding to the inspection hole (33). The presence or absence of the discharge valve is inspected by inserting the inspection member into the inspection hole and by an insertion depth of the inspection member.

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 relates to the technical field of intelligent inspection system and multi-device cooperative scheduling, and discloses an inspection method based on multi-device cooperation. Modeling inspection tasks and equipment in a unified manner in a scheduling period, registering time, types and maximum operation duration parameters, and constructing an executable relation of the task equipment; calculating residual response time, and generating normalized task priority according to the residual response time and the execution duration; and setting task unique allocation, equipment maximum load and task performability constraint, introducing Lagrange optimization, selecting tasks for equipment according to task density, iteratively updating until convergence, generating an execution sequence sorted according to residual response time, and issuing the execution sequence. Through the steps, task attributes are unified, the equipment capacity is quantified, unbalance caused by single time or duration sorting is avoided, timeliness and load are considered under multiple constraints, tasks can be executed, the equipment is not overloaded, and the resource utilization rate and the task completion rate are increased.

The invention discloses a switching station optical distribution equipment inspection method and system, and belongs to the technical field of electric power inspection. According to the invention, the to-be-inspected target switching station is determined through the real-time position of the inspector, so that the manual station searching time is effectively shortened; the appearance identification result, the indicating lamp identification result and the equipment model identification result of the optical distribution equipment are determined by identifying the image of the optical distribution equipment, so that the inspection deviation caused by manual inspection is reduced, and the missing inspection and false inspection of the optical distribution equipment are avoided; then, the inspection flow of the optical distribution equipment is determined according to the equipment model identification result, so that inspection personnel are guided to inspect the optical distribution equipment, the standardization and consistency of inspection of the optical distribution equipment are ensured, and low inspection efficiency caused by manual consulting of paper documents is avoided; and then an inspection report is generated, so that the integrity and specification of inspection data are ensured, and a basis is provided for long-term inspection of switching station optical distribution equipment. Therefore, the accuracy and efficiency of inspection of the optical distribution equipment of the switching station are improved.

The invention provides a three-dimensional steel structure full-coverage path planning inspection method based on topology reconstruction, and relates to the technical field of industrial robots, and the method comprises the steps: obtaining a model file of a to-be-inspected three-dimensional steel structure; constructing an original vertex set and an original edge set according to the model file; performing adaptive clustering on each original vertex in the original vertex set through a density-based spatial clustering algorithm to obtain a cluster; taking the geometric center of the original vertex in each cluster as a topological node, and establishing a mapping relation between the original vertex and the topological node; carrying out topology reconstruction on the original edge set, and constructing an undirected weighted graph; stitching non-connected sub-graphs in the undirected weighted graph through a K-dimensional tree and a minimum spanning tree algorithm to obtain a fully connected graph; generating a full-coverage continuous path sequence through a Chinese postman algorithm; the full-coverage continuous path sequence is converted into a three-dimensional space coordinate sequence, and the three-dimensional steel structure to be inspected is inspected according to the three-dimensional space coordinate sequence.

The invention discloses an automobile aluminum ornament product quality inspection method based on big data, particularly relates to the field of quality management and quality inspection decision, and is used for solving the problem that existing aluminum ornament quality inspection depends on fixed rules and experience judgment and is difficult to dynamically optimize quality inspection performance and resource allocation in combination with after-sales quality information. According to the method, structured processing and damage mode division are performed on vehicle after-sales damage data, key process features are extracted in combination with production process time sequence data, and a defect early propagation model is constructed, so that prediction of occurrence probabilities of different defect types is realized; on the basis, a multi-layer hierarchical quality inspection strategy is dynamically generated according to the defect risk level, quality inspection execution cost and historical quality loss cost are integrated, and quality inspection performance indexes of a next production cycle are output through multi-objective optimization, so that the scientificity and decision value of quality risk management are improved while the cost is controlled, and the quality risk management efficiency is improved. Therefore, the management requirements of refined operation and cost constraint are met.

The invention relates to the technical field of industrial automation, in particular to an automatic inspection method and device in a complex environment, a storage medium and related equipment, data acquired by different sensors can be subjected to space-time synchronization to improve dynamic positioning precision, and when part of the sensors fail, a function can still be maintained through synchronized redundant data, so that the reliability of the system is improved. The robustness of the system is effectively enhanced, and the data processing delay can be reduced through space-time synchronization, so that the real-time control requirement of a dynamic scene is met; locally updating the newly acquired global map according to the static characteristics and the target attitude data, thereby reducing the calculation amount, improving the accuracy of navigation positioning, and adapting to a complex environment; the current positioning error is corrected according to the target point cloud data and the target image data, so that the positioning accuracy is further improved, and the inspection path and the control instruction are generated based on the corrected positioning result and the target map, so that the operation risk is effectively reduced while automatic inspection is realized.

The invention relates to a freight train inspection method, device and equipment and an inspection robot. The method comprises the following steps: acquiring inspection environment data corresponding to a to-be-inspected freight train of an inspection robot, wherein the inspection environment data comprises image data and point cloud data; performing path planning processing according to the image data and the point cloud data to obtain a target inspection path from the current position of the inspection robot to the target inspection site; and controlling the inspection robot to go to the target inspection site based on the target inspection path, performing inspection from the target inspection site to obtain target inspection data, and sending the target inspection data to the master console. By adopting the method, the inspection reliability can be improved.

The invention discloses an automatic inspection method suitable for a building heating and ventilation system before intelligent regulation and control input, and the method comprises the steps: carrying out the standardized definition of a connection relation and associated equipment related to the building heating and ventilation system, obtaining a standardized construction building heating and ventilation system process description language, and storing the language in a database; performing algorithm availability selection based on the building heating and ventilation system, and determining a corresponding regulation and control algorithm and input and output parameters; based on a parameter dependency relationship dynamic verification mechanism oriented to a physical link of the building heating and ventilation system, performing dependency relationship verification on the input and output parameters; and carrying out regulation and control adaptability data baseline evaluation on the input and output parameters of which the dependency relationship verification is successful, and carrying out control logic simulation and verification by calling an API (Application Program Interface). According to the method, an AI algorithm user or developer can be efficiently helped to perform full self-inspection before the algorithm is input, and the reliability before the algorithm is input is guaranteed; and meanwhile, related AI algorithm practitioners are helped to optimize algorithm engineering.

The invention relates to the technical field of mine intelligent operation and maintenance and inspection robots, in particular to an inspection method, an inspection system and an inspection robot for a mining belt conveyor, which are used for acquiring temperature and vibration parameters along the line of the mining belt conveyor in real time; acquiring a target position point where an abnormal event occurs by using the temperature and vibration parameters of each position along the mining belt conveyor; obtaining a target inspection robot based on the distance between the target position point where the abnormal event occurs and all inspection robots along the mining belt conveyor and the residual electric quantity of each inspection robot; acquiring an optimal path from the target inspection robot to the target position point; the target inspection robot reinspects the target position after reaching the target position according to the optimal path, and the fault type of the target position point is determined; the speed of finding and responding to events such as conveyor belt deviation, bearing faults and temperature abnormity is remarkably increased, and the problems that a traditional system is limited in coverage and slow in response are solved.

The invention relates to an anti-misoperation intelligent inspection method and system for secondary equipment of a transformer substation and a medium. The method comprises the following steps: acquiring operation state data of a secondary soft pressing plate and primary equipment; establishing an equipment relation model based on a power dispatching procedure, a relay protection procedure and a substation field operation procedure; constructing an anti-error rule base; when a switching operation instruction of the transformer substation is received, simulation rehearsal is carried out on the operation sequence based on the anti-error rule base and the operation state data of the secondary soft pressing plate and the primary equipment; the primary equipment and the secondary equipment are operated based on the operation sequence of the simulation rehearsal, and an inspection report is generated; model decomposition, graph-model matching, rule calling and the like of the transformer substation are achieved through the topology technology, misjudgment prevention of primary and secondary equipment operation is further achieved, and it is effectively avoided that a protection pressing plate is missed in the primary equipment operation process; the method has the advantages that the equipment relation model and the anti-error rule base are established, the substation model decomposition and anti-error judgment are realized, and the protection pressing plate is effectively prevented from being missed to be put and withdrawn.

The invention discloses an intelligent inspection method and system for an all-terrain self-adaptive hydroelectric AI bionic robot, and relates to the technical field of robots, and the method comprises the steps: constructing a terrain stiffness matrix based on a laser radar, an IMU and a foot end force sensor, and driving a bionic joint group to carry out the intelligent switching of a wheel mode, a six-foot gait mode and an adsorption mode; for equipment defect identification, visible light, infrared and laser point cloud data are fused, and crack / corrosion classification is realized by adopting a contrast learning migration model; a dynamic risk grid map is generated in combination with the equipment fault history, the environment corrosion rate and the task emergency degree, the inspection path is optimized through an MOEA / D-TOPSIS algorithm, and multi-target balance of energy consumption and path length is achieved on the premise that the coverage rate is guaranteed; the positioning system integrates Beidou RTK, UWB anchor points and inertial navigation, and combines a dynamic SLAM enhancement technology, so that centimeter-level positioning precision under strong electromagnetic interference and satellite rejection environments is ensured, and an all-weather and all-terrain intelligent inspection solution is provided for hydroelectric facilities.

The automobile skylight glass assembly shaping detection device provided by the invention comprises a workbench, a glass assembly outer contour and hole position detection unit and a glass assembly pressing shaping and surface difference detection unit, and the workbench comprises a frame and a workbench surface arranged on the frame. The glass assembly outer contour and hole position detection unit is arranged on the workbench, the glass assembly pressing shaping and surface difference detection unit is arranged on the workbench, the invention further provides a detection method of the shaping detection device for the automobile skylight glass assembly, shaping detection can be conveniently conducted on the automobile skylight glass assembly, and the detection efficiency is improved. The curvature and flatness of the automobile skylight glass assembly can be conveniently detected, the assembling gap between the automobile skylight glass and the skylight frame can be conveniently detected, the detection efficiency can be improved, and the outgoing quality and reliability of the automobile skylight glass assembly can be improved.

This invention provides an inspection system, inspection method, and program that are advantageous for determining multiple control parameters that may affect the transport of electronic components. [Solution] The control unit of the inspection system acquires transport status information including a plurality of interrelated control parameters and cumulative data regarding transport results (S1), derives a parameter set that includes a plurality of control parameters and shows the smallest uninspected rate from the transport status information using Gaussian process regression (S2, S3), and updates a plurality of control parameters for the transport device and peripheral equipment respectively based on the derived parameter set (S4).

The invention discloses a fan intelligent inspection method based on electrical detection, relates to the technical field of fan detection, and solves the problem that an image acquisition strategy cannot be matched with the real health state of equipment in the prior art. According to the method, the multi-source electrical data are collected in real time, the fault probability is predicted, the trigger condition of image collection is converted from passive waiting to active pre-judgment, and invalid inspection in the healthy period is avoided; secondly, fault type-observability score mapping is introduced, thermal imaging identifiability quantification of different defects is brought into decision making, high-risk and easy-to-image faults trigger emergency single-time collection, the frequency of low-observability faults is automatically reduced, and differentiated accurate putting of collection resources is achieved; finally, electrical early warning and image forensics are directly coupled in a feature layer by fusing the fault probability and the observability priority index, so that the problem of double waste in a traditional mode is fundamentally eradicated on the premise of ensuring high reliability of the offshore wind turbine.

The disclosure provides a large model-based quality inspection method, device, equipment, storage medium and program, relating to the technical field of data processing, specifically relating to artificial intelligence, deep learning and large language model technology, which can be applied in the field of AI medical technology, wherein the method comprises: obtaining to-be-inspected data of a target platform and a target quality inspection prompt template; wherein the target quality inspection prompt template comprises quality inspection task instructions and quality inspection question classification content; splicing the to-be-inspected data and the target quality inspection prompt template to obtain a target quality inspection prompt; inputting the target quality inspection prompt into a target large model to automatically inspect the to-be-inspected data according to the target quality inspection prompt through the target large model, and obtaining first quality inspection data. The embodiment of the disclosure can reduce the artificial quality inspection cost of platform quality inspection and improve the efficiency and accuracy of platform quality inspection.

To provide a technique advantageous for detecting a mistake of a mold to be mounted on an imprint device and a mistake of upper and lower pattern surfaces of the mold mounted on the imprint device.SOLUTION: To provide an inspection method for inspecting a state of a mold for imprinting a material on a substrate. The mold has a first surface including a region to be in contact with the material and a second surface opposite to the first surface. The inspection method includes a measurement step of performing height measurement of each of a first region, which is a central region, and a second region, which is a region outside the first region, of the first surface of the mold, and a determination step of determining, based on a result of the height measurement of each of the first region and the second region, whether the first surface and the second surface of the mold face in a correct direction, and determining whether a shape of the mold conforms to an assumed shape.SELECTED DRAWING: Figure 3