Method and device for deforming inspection of compressed air energy storage cavern, equipment and medium

Abstract

This invention relates to the field of electronic technology and discloses a method, apparatus, equipment, and medium for deformation inspection of compressed air energy storage chambers. The method includes: acquiring regional point cloud data of multiple key areas in a target chamber and the theoretical positions of monitoring points in each key area, wherein each key area contains multiple monitoring points and the setting method of the monitoring points is different in different key areas; determining the local point cloud data of the corresponding monitoring point in the regional point cloud data with the theoretical position of each monitoring point as the center and the target value as the radius; determining the centroid position based on the local point cloud data of each monitoring point; calculating a global transformation matrix based on the centroid position and theoretical position of each monitoring point; calibrating the local point cloud data of each key area based on the global transformation matrix to obtain the calibrated absolute position of each monitoring point; and determining the deformation inspection result of the corresponding key area based on the absolute positions of multiple monitoring points in each key area and the pre-acquired historical inspection positions.

Description

Technical Field

[0001] This invention relates to the field of electronic technology, specifically to a method, apparatus, equipment, and medium for deformation inspection of compressed air energy storage chambers. Background Technology

[0002] Compressed air energy storage chambers, as special enclosed spaces, operate under a constant high-pressure circulating environment, resulting in significant structural deformation. Failure to detect this deformation promptly and accurately can lead to safety accidents. Current technologies often employ single-point sensor monitoring and manual inspection to assess chamber deformation. Single-point sensor monitoring cannot comprehensively reflect the overall deformation of the chamber, failing to meet the demands for high-precision, comprehensive monitoring. Manual inspection requires personnel to enter the high-pressure chamber, presenting challenges such as high safety risks, low efficiency, and limited monitoring range. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, and medium for deformation inspection of compressed air energy storage chambers, in order to solve the problems of high risk, low efficiency, and limited monitoring range associated with manual inspection in related technologies.

[0004] In a first aspect, the present invention provides a deformation inspection method for a compressed air energy storage chamber. The method includes: acquiring regional point cloud data of multiple key areas in the target chamber and the theoretical positions of monitoring points in each key area, wherein each key area contains multiple monitoring points and the monitoring points are set up differently in different key areas; determining the local point cloud data of the corresponding monitoring point in the regional point cloud data with the theoretical position of each monitoring point as the center and the target value as the radius; determining the centroid position based on the local point cloud data of each monitoring point; calculating a global transformation matrix based on the centroid position and theoretical position of each monitoring point; calibrating the local point cloud data of each key area based on the global transformation matrix to obtain the calibrated absolute position of each monitoring point; and determining the deformation inspection result of the corresponding key area based on the absolute positions of multiple monitoring points in each key area and the pre-acquired historical inspection positions.

[0005] This invention provides a deformation inspection method for compressed air energy storage chambers. It acquires point cloud data of multiple key areas within the chamber and the theoretical positions of differentiated monitoring points, breaking the limitations of single-point sensors that can only reflect local deformation. This method comprehensively presents the overall deformation state of the chamber, meeting comprehensive monitoring needs. Furthermore, by determining the local point cloud of the monitoring point with the theoretical position as the center and the target value as the radius, and combining the centroid position of the local point cloud with the calculation of the centroid position and theoretical position, the absolute position of the monitoring point is calibrated using a global transformation matrix. This effectively eliminates systematic errors caused by scanning and installation, weakens the random errors of single point clouds, significantly improves monitoring accuracy, and solves the problems of incomplete data from single-point sensors and the influence of human factors on the accuracy of manual measurements. The method relies entirely on automated point cloud data acquisition and processing, eliminating the need for personnel to enter the high-pressure chamber for inspections. This fundamentally eliminates the safety risks of manual inspections and replaces the tedious operations of manual on-site measurement and recording, solving the drawbacks of low efficiency and limited monitoring range of manual inspections. Finally, by comparing the calibrated absolute position with the historical inspection position, the deformation result is determined. This can accurately capture the deformation characteristics of each key area, providing accurate and systematic data support for the chamber deformation monitoring. It can promptly detect potential deformation hazards in the chamber under high pressure, avoid safety accidents caused by untimely deformation detection, and ensure the stable operation of the chamber.

[0006] In one optional implementation, the step of acquiring local point cloud data of multiple key areas in the target chamber and the theoretical positions of monitoring points in each key area includes: acquiring discrete three-dimensional point cloud data of the target chamber, geometric parameters of the target chamber, location information of multiple key areas, and theoretical positions of monitoring points in each key area. The discrete three-dimensional point cloud data is obtained by scanning the inner wall of the target chamber using a three-dimensional laser scanner. The discrete three-dimensional point cloud data is used to characterize three-dimensional point cloud data at different scanning positions. The discrete three-dimensional point cloud data is stitched together based on the geometric parameters to obtain the overall point cloud data of the target chamber. Based on the location information of each key area, the corresponding regional point cloud data of the key area is determined in the overall point cloud data.

[0007] The method provided in this optional implementation utilizes a 3D laser scanner to acquire a discrete point cloud covering the entire interior wall of the chamber. This point cloud is then stitched together with the chamber's geometric parameters to ensure it closely matches the actual structural features of the chamber, eliminating coordinate deviations in the discrete point cloud and achieving full coverage of the chamber's point cloud. This solves the problem of incomplete coverage in traditional monitoring data. Furthermore, based on key area location information, point cloud data for each region is precisely extracted from the overall point cloud, achieving precise regional division of the point cloud. This effectively avoids cross-regional point cloud interference and provides a clean and targeted data source for subsequent local point cloud extraction and coordinate calculation at monitoring points.

[0008] In one optional implementation, discrete three-dimensional point cloud data is stitched together based on geometric parameters to obtain overall point cloud data of the target chamber. This includes: denoising the discrete three-dimensional point cloud data to obtain effective discrete point cloud data; determining the design geometric features of the target chamber based on the geometric parameters; extracting point cloud geometric feature information corresponding to the geometric parameters from the effective discrete point cloud data; matching the point cloud geometric feature information with the design geometric features to obtain a matching result; and stitching together the discrete three-dimensional point cloud data based on the matching result to obtain overall point cloud data of the target chamber.

[0009] The method provided in this optional implementation significantly improves the accuracy and completeness of the overall point cloud data of the chamber through a standardized and refined point cloud stitching process, laying a high-quality data foundation for subsequent deformation monitoring. First, the discrete point cloud is denoised to remove invalid interference points, ensuring the validity of the point cloud data and preventing noise points from affecting the accuracy of subsequent stitching and analysis. Then, the design geometric features are determined based on the geometric parameters of the chamber, and corresponding geometric features are extracted from the effective point cloud for precise matching. This ensures that the point cloud stitching is based on the actual design structure of the chamber, effectively eliminating coordinate deviations of discrete point clouds at each scanning position and ensuring a high degree of consistency between the stitched overall point cloud and the actual spatial structure of the chamber. The stitching is completed based on the feature matching results, further improving the spatial continuity of the overall point cloud and avoiding problems such as stitching gaps and regional misalignment, achieving complete coverage of the entire chamber's point cloud.

[0010] In one optional implementation, the step of determining the deformation inspection result of the corresponding key area based on the absolute position of multiple monitoring points within each key area and the pre-acquired historical inspection positions includes: determining the deformation of the corresponding monitoring point based on the absolute position of each monitoring point and the historical inspection position; determining the deformation rate of the corresponding monitoring point based on the deformation of each monitoring point and the inspection interval; determining the deformation distribution uniformity index value of the corresponding key area based on the deformation of multiple monitoring points within each key area; and determining the deformation inspection result of the corresponding key area based on the deformation, deformation rate, and deformation distribution uniformity index value of multiple monitoring points within each key area.

[0011] The method provided in this optional implementation determines the deformation amount by comparing the absolute and historical positions of monitoring points, calculates the deformation rate by combining the inspection interval, and obtains the deformation distribution uniformity index based on the deformation amount of monitoring points within the region. It comprehensively depicts the true deformation state of each key area from three dimensions: static deformation amount, dynamic deformation trend, and regional deformation distribution. This overcomes the limitations of traditional monitoring, which only focuses on a single deformation amount and cannot reflect the deformation development law and distribution characteristics. Simultaneously, it performs multi-index calculations and result judgments on a key area-by-area basis, aligning with the structural characteristics of different areas of the chamber, achieving refined and targeted deformation monitoring and avoiding the ambiguity of overall analysis. Furthermore, the obtained three types of quantitative indicators—deformation amount, deformation rate, and uniformity—can be directly used as the core basis for deformation risk assessment, allowing inspection results to effectively serve the risk assessment and emergency response of the chamber, significantly improving the application value of monitoring data and adapting to the actual needs of safe operation and maintenance of chambers under high-pressure circulating environments.

[0012] In one optional implementation, the step of determining the deformation inspection result of a corresponding key area based on the deformation, deformation rate, and deformation distribution uniformity index values ​​of multiple monitoring points in each key area includes: obtaining index weight information; determining the risk assessment value of the corresponding key area based on the deformation, deformation rate, deformation distribution uniformity index values, and index weight information of multiple monitoring points in each key area; and determining the deformation inspection result of the corresponding key area based on the risk assessment value of each key area.

[0013] The method provided in this optional implementation determines deformation inspection results by introducing indicator weights and quantifying risk assessment values, making the chamber deformation assessment more scientific, accurate, and aligned with engineering realities. Introducing indicator weight information distinguishes the importance of deformation amount, deformation rate, and deformation distribution uniformity, aligning with the risk patterns of chamber structural deformation and avoiding the one-sidedness of equal judgments based on multiple indicators. This makes the assessment more consistent with the safety requirements of high-pressure operation in the chamber. By combining multiple indicators with weights to calculate a unified risk assessment value, multi-dimensional deformation data is transformed into quantifiable values, providing clear criteria for judging deformation risks in key areas, making the results more intuitive and comparable across different regions. Simultaneously, determining inspection results based on risk assessment values ​​provides a clear basis for subsequent graded early warning and targeted operation and maintenance, effectively avoiding missed hazard assessments or excessive repairs, and improving the scientific nature of chamber safety management.

[0014] Secondly, the present invention provides a deformation inspection system for a compressed air energy storage chamber. The system includes: a laser scanner and a monitoring terminal; the laser scanner is connected to an inspection track in the target chamber via a sliding component, and is used to slide along the inspection track under the drive of the sliding component, and to collect three-dimensional point cloud data inside the target chamber during the sliding process; the monitoring terminal is communicatively connected to the laser scanner and is used to execute the deformation inspection method for the compressed air energy storage chamber of the first aspect or any corresponding embodiment described above.

[0015] The deformation inspection system for compressed air energy storage chambers provided by this invention achieves hardware-based, automated, and integrated deformation monitoring of the chamber through the coordinated operation of a laser scanner, a sliding assembly, an inspection track, and a monitoring terminal. The laser scanner automatically slides along the inspection track, driven by the sliding assembly, to complete the full-area acquisition of three-dimensional point cloud data inside the chamber. The entire process requires no personnel to enter the high-pressure enclosed chamber, eliminating the safety risks of manual inspection at its source, while simultaneously achieving efficient and comprehensive point cloud data acquisition. The monitoring terminal communicates and links with the laser scanner, automatically executing the entire deformation inspection method, completing the entire data processing process including point cloud stitching, region segmentation, coordinate calibration, deformation calculation, and risk assessment. This deep integration of hardware acquisition and software analysis ensures both monitoring accuracy and coverage, while also enabling unmanned and routine operation of the inspection work. It can promptly and accurately identify potential deformation hazards in the chamber, significantly improving the intelligence level and safety assurance capabilities of chamber deformation monitoring under high-pressure environments.

[0016] Thirdly, the present invention provides a deformation inspection device for a compressed air energy storage chamber. The device includes: an acquisition module for acquiring regional point cloud data of multiple key areas in the target chamber and the theoretical positions of monitoring points in each key area, wherein each key area contains multiple monitoring points, and the monitoring points are set up differently in different key areas; a first determination module for determining the local point cloud data of the corresponding monitoring point in the regional point cloud data, with the theoretical position of each monitoring point as the center and the target value as the radius; a second determination module for determining the centroid position based on the local point cloud data of each monitoring point; a calculation module for calculating a global transformation matrix based on the centroid position and theoretical position corresponding to different monitoring points; a calibration module for calibrating the local point cloud data of each key area based on the global transformation matrix to obtain the calibrated absolute position of each monitoring point; and a third determination module for determining the deformation inspection result of the corresponding key area based on the absolute positions of multiple monitoring points within each key area and pre-acquired historical inspection positions.

[0017] Fourthly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the deformation inspection method of the compressed air energy storage chamber described in the first aspect or any corresponding embodiment.

[0018] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the deformation inspection method for a compressed air energy storage chamber according to the first aspect or any corresponding embodiment described above.

[0019] In a sixth aspect, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the deformation inspection method for a compressed air energy storage chamber according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of a deformation inspection method for a compressed air energy storage chamber according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the deformation inspection method for a compressed air energy storage chamber according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the deformation inspection method for a compressed air energy storage chamber according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a deformation inspection system for a compressed air energy storage chamber according to an embodiment of the present invention; Figure 6 This is a structural block diagram of a deformation inspection device for a compressed air energy storage chamber according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0024] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0025] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the surface deformation monitoring method based on parameter search interval optimization depends is described here. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0026] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0027] Compressed air energy storage chambers, as special enclosed spaces, operate under a constant high-pressure circulating environment, resulting in significant structural deformation. Failure to detect this deformation promptly and accurately can lead to safety accidents. Current technologies often employ single-point sensor monitoring and manual inspection to assess chamber deformation. Single-point sensor monitoring cannot comprehensively reflect the overall deformation of the chamber, failing to meet the demands for high-precision, comprehensive monitoring. Manual inspection requires personnel to enter the high-pressure chamber, presenting challenges such as high safety risks, low efficiency, and limited monitoring range.

[0028] In view of this, this application provides a deformation inspection method for compressed air energy storage chambers, which can be applied to a server to achieve automatic deformation inspection of compressed air energy storage chambers. The method provided in this application acquires point cloud data of multiple key areas of the chamber and the theoretical positions of differentiated monitoring points, breaking the limitations of single-point sensors that can only reflect local point deformation. It can fully present the overall deformation state of the chamber, meeting the needs of comprehensive monitoring. Furthermore, by determining the local point cloud of the monitoring point with the theoretical position as the center and the target value as the radius, and combining the centroid position of the local point cloud, and then calculating the global transformation matrix based on the centroid position and the theoretical position to calibrate the absolute position of the monitoring point, the method effectively eliminates systematic errors caused by scanning and installation, weakens the random errors of single point clouds, significantly improves monitoring accuracy, and solves the problems of incomplete data from single-point sensors and the influence of human factors on the accuracy of manual measurements. The method relies entirely on automated point cloud data acquisition and processing, eliminating the need for personnel to enter the high-voltage chamber for inspections. This fundamentally eliminates the safety risks of manual inspections and replaces the tedious manual on-site measurement and recording, overcoming the drawbacks of low efficiency and limited monitoring range of manual inspections. Finally, the deformation results are determined by comparing the calibrated absolute position with historical inspection positions, accurately capturing the deformation characteristics of key areas. This provides precise and systematic data support for chamber deformation monitoring, enabling timely detection of potential deformation hazards in high-voltage environments, preventing safety accidents caused by delayed deformation detection, and ensuring the stable operation of the chamber.

[0029] According to an embodiment of the present invention, a deformation inspection method for a compressed air energy storage chamber is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides a deformation inspection method for a compressed air energy storage chamber, which can be used in the aforementioned server. Figure 2 This is a flowchart of a deformation inspection method for a compressed air energy storage chamber according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain regional point cloud data of multiple key areas in the target chamber and the theoretical location of monitoring points in each key area. Each key area contains multiple monitoring points, and the setting method of monitoring points in different key areas is different.

[0031] For example, the target chamber refers to the compressed air energy storage artificial chamber that requires deformation inspection and monitoring, and is the object of the entire monitoring; the key area refers to the core area within the chamber that is subject to high stress and prone to deformation, such as the arch, left wall, right wall, bottom slab, and the junction of the arch and wall, which are the key structural units to be monitored; the regional point cloud data refers to the local three-dimensional point cloud data of a certain key area; the monitoring point refers to the core calculation point located within the key area for calculating deformation, and its positional changes are used to determine whether the chamber is deformed and the extent of the deformation; the theoretical position of the monitoring point refers to the ideal coordinates of the monitoring point on the chamber design drawings or three-dimensional model. In this embodiment, the design three-dimensional model of the chamber construction stage is used as the benchmark, and the theoretical coordinates (X0, Y0, Z0) of each monitoring point are calculated in the design model to form a theoretical coordinate library. Identify the key stress-bearing and easily deformable parts of the chamber structure as the basis for selecting monitoring points. The specific rules are as follows: Divide the three-dimensional model of the chamber into 6 core areas—the arch area, the left wall area, the right wall area, the bottom plate area, the front wall area, and the rear wall area—and 4 key transition areas—the junction of the arch and the left wall, the junction of the arch and the right wall, the junction of the left wall and the bottom plate, and the junction of the right wall and the bottom plate. For the vault area, one monitoring point is set every 5m along the length of the chamber, with each monitoring point being the geometric center of the vault at that cross-section. For the sidewall area (left / right), grid-like monitoring points are set every 5m along the length of the chamber and every 3m vertically (from top to bottom). Each sidewall is distributed according to a "length × height" grid, such as 20 length nodes and approximately 10 height nodes for a 100m long chamber, for a total of 200 monitoring points per sidewall. For the floor slab area, grid-like monitoring points are set every 5m along the length of the chamber and every 4m in the width direction, covering the entire floor slab area. For critical turning areas, one monitoring point is set every 5m along the length of the chamber, precisely corresponding to the edge position at the junction. For the end wall area (front / rear), nine monitoring points are set according to a "3×3" grid, covering the center, four corners, and midpoint of the edge of the end wall.

[0032] Step S202: Using the theoretical location of each monitoring point as the center and the target value as the radius, determine the local point cloud data of the corresponding monitoring point in the regional point cloud data.

[0033] For example, in this embodiment of the application, coordinate calibration is required due to factors such as track installation errors and scanning reference deviations. Using the theoretical coordinates of the monitoring point as the spatial center and the set target value as the radius, a spherical search range is delineated. Within the point cloud data of this key area, all point clouds falling within this spherical range are selected; these selected point clouds constitute the local point cloud data corresponding to the monitoring point.

[0034] Step S203: Determine the centroid position based on the local point cloud data of each monitoring point.

[0035] For example, in this embodiment of the application, the arithmetic mean of the three-dimensional coordinates of all points in the local point cloud corresponding to the monitoring point is calculated, and the calculated average coordinates are the centroid position of the monitoring point.

[0036] Step S204: Calculate the global transformation matrix based on the centroid position and theoretical position corresponding to different monitoring points.

[0037] For example, in this embodiment of the application, the centroid position of each monitoring point is used as the actual point and the theoretical position is used as the reference point to form multiple sets of one-to-one matching point pairs; then, the rotation and translation parameters that make the overall alignment of these matching points optimal are calculated by the point cloud registration algorithm; finally, the rotation and translation parameters are combined to obtain the global transformation matrix.

[0038] Step S205: Based on the global transformation matrix, calibrate the local point cloud data of each key area to obtain the calibrated absolute position of each monitoring point.

[0039] For example, in this embodiment of the application, the centroid position of each monitoring point is uniformly rotated and translated using a global transformation matrix to correct the overall system deviation. The coordinates obtained after the transformation are the absolute position of the monitoring point after calibration.

[0040] Step S206: Determine the deformation inspection results of the corresponding key areas based on the absolute positions of multiple monitoring points within each key area and the historical inspection positions obtained in advance.

[0041] For example, in this embodiment of the application, the deformation is first calculated based on the current absolute position and historical inspection position of each monitoring point, and then the deformation rate is obtained by combining the inspection interval time. At the same time, the deformation distribution uniformity index is determined based on the deformation of all monitoring points in the area. Finally, the three indicators are combined to complete the quantitative evaluation and obtain the deformation inspection results of the corresponding key area.

[0042] The deformation inspection method for compressed air energy storage chambers provided in this embodiment acquires point cloud data of multiple key areas of the chamber and the theoretical positions of differentiated monitoring points. This breaks through the limitations of single-point sensors that can only reflect local deformation, and can fully present the overall deformation state of the chamber, meeting the needs of comprehensive monitoring. Furthermore, by determining the local point cloud of the monitoring point with the theoretical position as the center and the target value as the radius, and combining the centroid position of the local point cloud, a global transformation matrix is ​​calculated based on the centroid position and the theoretical position to calibrate the absolute position of the monitoring point. This effectively eliminates systematic errors caused by scanning and installation, weakens the random errors of single point clouds, significantly improves monitoring accuracy, and solves the problems of incomplete data from single-point sensors and the influence of human factors on the accuracy of manual measurements. The method relies entirely on automated point cloud data acquisition and processing, eliminating the need for personnel to enter the high-pressure chamber for inspection, thus fundamentally eliminating the safety risks of manual inspection. It also replaces the tedious operation of manual on-site measurement and recording, solving the drawbacks of low efficiency and limited monitoring range of manual inspection. Finally, by comparing the calibrated absolute position with the historical inspection position, the deformation result is determined. This can accurately capture the deformation characteristics of each key area, providing accurate and systematic data support for the chamber deformation monitoring. It can promptly detect potential deformation hazards in the chamber under high pressure, avoid safety accidents caused by untimely deformation detection, and ensure the stable operation of the chamber.

[0043] This embodiment provides a deformation inspection method for a compressed air energy storage chamber, which can be used in the aforementioned server. Figure 3 This is a flowchart of a deformation inspection method for a compressed air energy storage chamber according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain regional point cloud data of multiple key areas in the target chamber and the theoretical locations of monitoring points in each key area. Each key area contains multiple monitoring points, and the setting method of monitoring points differs in different key areas. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0044] Step S302: Using the theoretical location of each monitoring point as the center and the target value as the radius, determine the local point cloud data of the corresponding monitoring point in the regional point cloud data. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0045] Step S303: Determine the centroid location based on the local point cloud data of each monitoring point. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0046] Step S304: Calculate the global transformation matrix based on the centroid positions and theoretical positions corresponding to different monitoring points. For details, please refer to [link to relevant documentation]. Figure 2Step S204 of the illustrated embodiment will not be described again here.

[0047] Step S305: Based on the global transformation matrix, calibrate the local point cloud data of each key area to obtain the calibrated absolute position of each monitoring point. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0048] Step S306: Determine the deformation inspection results of the corresponding key area based on the absolute positions of multiple monitoring points in each key area and the historical inspection positions obtained in advance.

[0049] Specifically, step S306 includes: Step S3061: Determine the deformation of the corresponding monitoring point based on the absolute position of each monitoring point and the historical inspection position.

[0050] For example, the spatial distance obtained by subtracting the three-dimensional coordinates of the current absolute position of the monitoring point from the historical inspection position and calculating the three-dimensional Euclidean distance between the two points is the deformation of the monitoring point.

[0051] Step S3062: Determine the deformation rate of the corresponding monitoring point based on the deformation of each monitoring point and the inspection interval.

[0052] For example, the deformation rate of a monitoring point is obtained by dividing the deformation of the monitoring point by the interval between two inspections.

[0053] Step S3063: Determine the deformation distribution uniformity index value of the corresponding key area based on the deformation of multiple monitoring points in each key area.

[0054] For example, first calculate the mean and standard deviation of the deformation of all monitoring points in the key area, and then divide the standard deviation by the mean to obtain the coefficient of variation, which is the deformation distribution uniformity index value.

[0055] Step S3064: Based on the deformation, deformation rate, and deformation distribution uniformity index values ​​of multiple monitoring points in each key area, determine the deformation inspection results of the corresponding key area.

[0056] In some optional implementations, step S3064 above includes: Step a1: Obtain indicator weight information.

[0057] For example, the indicator weight information can be determined according to actual needs, and this application embodiment does not impose specific limitations. In this application embodiment, the importance of each evaluation indicator is quantified by the analytic hierarchy process (AHP). Six experts with more than 10 years of experience in structural safety research or engineering practice of compressed air energy storage chambers are invited, covering three fields: structural engineering, geotechnical engineering, and energy storage technology, to ensure the professionalism of the evaluation. The 1-9 scale method is used, allowing each expert to compare the importance of the three indicators in the criterion layer pairwise, constructing a judgment matrix M (n×n, n=3). The maximum eigenvalue of the judgment matrix M is calculated. λ The criteria are: max, consistency index CI, random consistency index RI, and consistency ratio CR. For each expert's judgment matrix that meets the consistency requirements, the eigenvector corresponding to its largest eigenvalue is calculated. After normalizing the eigenvector, the indicator weight vector given by that expert is obtained. Based on the expert authority coefficient, the weight vectors of the 6 experts are weighted and averaged to obtain the final criterion layer indicator weights: B1 (deformation amount) weight 0.5, B2 (deformation rate) weight 0.3, and B3 (deformation distribution uniformity) weight 0.2.

[0058] Step a2: Based on the deformation, deformation rate, deformation distribution uniformity index values ​​and index weight information of multiple monitoring points in each key area, determine the risk assessment value of the corresponding key area.

[0059] For example, in this embodiment of the application, the mean values ​​of deformation, deformation rate and deformation distribution uniformity of each monitoring point in the key area are calculated, and then multiplied by the corresponding index weights and summed. The final value is the risk assessment value of the key area.

[0060] Step a3: Determine the deformation inspection results of the corresponding key areas based on the risk assessment values ​​of each key area.

[0061] For example, in this embodiment of the application, based on the design specifications of compressed air energy storage power stations and the bearing capacity limits of the chamber structure materials, a three-level risk level standard is established for three indicators: deformation amount (B1), deformation rate (B2), and deformation distribution uniformity (B3). Low risk is C1, medium risk is C2, and high risk is C3. The specific thresholds are: deformation amount ≤ 3mm is C1, 3mm < deformation amount ≤ 5mm is C2, and deformation amount > 5mm is C3; deformation rate ≤ 0.1mm / d is C1, 0.1mm / d < rate ≤ 0.3mm / d is C2, and rate > 0.3mm / d is C3; deformation distribution uniformity is measured by the coefficient of variation, with coefficient of variation ≤ 0.2 for C1, 0.2 < coefficient of variation ≤ 0.4 for C2, and coefficient of variation > 0.4 for C3.

[0062] Triangular membership functions are used to characterize the degree of membership of each indicator to different risk levels. The measured deformation data (b1,b2,b3) of a single key monitoring point are substituted into the membership function of the corresponding indicator to calculate its membership degree to the three levels C1, C2, and C3, and then a 3×3 fuzzy evaluation matrix R is constructed.

[0063] The preset index weight vector W=[0.5,0.3,0.2] and the fuzzy evaluation matrix R are multiplied by a weighted average method to obtain the comprehensive membership vector B.

[0064] Finally, the risk level is determined according to the principle of maximum membership degree, and the risk level corresponding to the element with the largest value in the comprehensive membership degree vector B is selected as the final result; if the membership degree difference between any two levels is ≤0.1, in order to ensure the conservatism of the risk warning, the higher risk level is directly selected as the judgment result.

[0065] Five actual cases of compressed air artificial chambers that have undergone deformation (including 1-2 cases each of low, medium and high risk) were selected. The deformation data of the cases were substituted into the model to calculate the risk level and compare it with the actual safety status. If the model's judgment result is less than 90% consistent with the actual status, the scale value of the judgment matrix or the threshold parameter of the membership function was adjusted, and the weights and membership degrees were recalculated until the consistency was ≥90% to ensure the engineering applicability of the model.

[0066] This embodiment provides a deformation inspection method for a compressed air energy storage chamber, which can be used in the aforementioned server. Figure 4 This is a flowchart of a deformation inspection method for a compressed air energy storage chamber according to an embodiment of the present invention, as shown below. Figure 4 As shown, the process includes the following steps: Step S401: Obtain regional point cloud data of multiple key areas in the target chamber and the theoretical location of monitoring points in each key area. Each key area contains multiple monitoring points, and the setting method of monitoring points in different key areas is different.

[0067] Specifically, step S401 includes: Step S4011: Obtain discrete three-dimensional point cloud data of the target chamber, geometric parameters of the target chamber, location information of multiple key areas, and theoretical positions of monitoring points in each key area. The discrete three-dimensional point cloud data is obtained by scanning the inner wall of the target chamber using a three-dimensional laser scanner. The discrete three-dimensional point cloud data is used to characterize the three-dimensional point cloud data at different scanning positions.

[0068] For example, a 3D laser scanner mounted on an inspection track is used to slide along the chamber and scan different positions on the inner wall to collect discrete 3D point cloud data; at the same time, the design geometric parameters of the target chamber and the preset position information of key areas such as the arch and side walls are extracted to complete the acquisition of the above three types of data.

[0069] Step S4012: Based on geometric parameters, the discrete three-dimensional point cloud data is stitched together to obtain the overall point cloud data of the target chamber.

[0070] In some optional implementations, step S4012 includes: Step b1 involves denoising the discrete 3D point cloud data to obtain effective discrete point cloud data.

[0071] For example, a multi-dimensional filtering algorithm is used to remove invalid noise points from the discrete point cloud and retain the valid point cloud that reflects the true structure of the chamber. The core implementation method is as follows: First, statistical filtering is used to calculate the mean and standard deviation of the distance of the point cloud in the neighborhood of each point, and a threshold is set to filter out and remove outlier noise points that deviate from the overall distribution; then, radius filtering is used to remove isolated noise points with the number of points in the neighborhood below the threshold, with each point as the center and a radius range adapted to the structure of the chamber; finally, redundant overlapping points caused by repeated scanning are removed. The remaining point cloud data after the above processing is the valid discrete point cloud data.

[0072] Step b2: Determine the design geometric features of the target chamber based on geometric parameters.

[0073] For example, based on the design geometric parameters of the target chamber, the theoretical standard geometric shape of the chamber is constructed, and the design geometric features that can characterize the standard structure of the chamber, such as the contour, axis, surface, and boundary position, are extracted.

[0074] Step b3: Extract point cloud geometric feature information corresponding to the geometric parameters from the effective discrete point cloud data.

[0075] For example, based on the geometric parameters of the target chamber (such as cross-sectional dimensions, axis orientation, radius of curvature, etc.), the types of point cloud geometric features to be extracted (such as contour lines, surface curvature, planar parameters, key point coordinates, etc.) are first determined; then, point cloud feature extraction algorithms (such as neighborhood curvature analysis, contour fitting, plane segmentation, etc.) are used to filter, identify and extract the feature information corresponding to the geometric parameters from the effective discrete point cloud data (such as the arched contour of the chamber roof, the plane normal vector of the side wall, the coordinates of the center line of the bottom plate, etc.).

[0076] Step b4: Match the point cloud geometric feature information with the designed geometric features to obtain the matching result.

[0077] For example, firstly, key geometric features (such as the contour, curvature, and central axis of key areas of the chamber) are extracted from the point cloud data, along with the theoretical geometric features of the corresponding areas in the design model. Then, a feature registration algorithm (such as the ICP iterative nearest point algorithm) is used to align the measured point cloud geometric features with the design geometric features as a reference, and the spatial deviation between the two is calculated. Finally, the matching result is output, including the feature alignment overlap, deviation values ​​of each dimension, and matching accuracy, which intuitively reflects the geometric differences between the measured point cloud and the design model.

[0078] Step b5: Based on the matching results, the discrete three-dimensional point cloud data is stitched together to obtain the overall point cloud data of the target chamber.

[0079] For example, based on the spatial transformation parameters (rotation and translation matrices) obtained by point cloud matching, the discrete three-dimensional point cloud data obtained from different scanning positions are sequentially transformed into coordinate systems so that all discrete point clouds are unified under the same global coordinate system. Then, all the transformed point cloud data are integrated and superimposed to finally form overall point cloud data covering the entire target chamber.

[0080] Step S4013: Based on the location information of each key region, determine the regional point cloud data of the corresponding key region in the overall point cloud data.

[0081] For example, based on the preset location information of each key region, the three-dimensional spatial range of each key region is defined; all point cloud coordinates in the overall point cloud data are traversed, and it is determined whether each one falls within the spatial range of the corresponding key region; all point cloud data that meet the conditions are filtered out and integrated to finally obtain the regional point cloud data corresponding to each key region.

[0082] Step S402: Using the theoretical location of each monitoring point as the center and the target value as the radius, determine the local point cloud data of the corresponding monitoring point in the regional point cloud data. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0083] Step S403: Determine the centroid location based on the local point cloud data of each monitoring point. For details, please refer to [link to relevant documentation]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0084] Step S404: Calculate the global transformation matrix based on the centroid positions and theoretical positions corresponding to different monitoring points. For details, please refer to [link to relevant documentation]. Figure 3 Step S304 of the illustrated embodiment will not be described again here.

[0085] Step S405: Based on the global transformation matrix, calibrate the local point cloud data of each key area to obtain the calibrated absolute position of each monitoring point. For details, please refer to [link to relevant documentation]. Figure 3Step S305 of the illustrated embodiment will not be described again here.

[0086] Step S406: Determine the deformation inspection results for the corresponding key areas based on the absolute positions of multiple monitoring points within each key area and the pre-acquired historical inspection positions. For details, please refer to [link to relevant documentation]. Figure 3 Step S305 of the illustrated embodiment will not be described again here.

[0087] This embodiment provides a deformation inspection system for a compressed air energy storage chamber. Figure 5 This is a schematic block diagram of the deformation inspection system for a compressed air energy storage chamber according to an embodiment of the present invention, as shown below. Figure 5 As shown, the system includes: a laser scanner 501 and a monitoring terminal 502; The laser scanner 501 is connected to the inspection track in the target chamber via a sliding assembly. It is used to slide along the inspection track under the drive of the sliding assembly and collect three-dimensional point cloud data inside the target chamber during the sliding process.

[0088] For example, in this embodiment, a 3D laser scanner with high-pressure resistance is selected, and the scanner's high-pressure resistance level is not lower than the maximum working pressure of the chamber. The 3D laser scanner is mounted on a sliding assembly on a track, allowing the scanner to move freely along the track, and the scanning angle can cover the inner wall of the chamber. The track is made of steel, and its surface is smoothed to reduce the friction between the sliding assembly and the track, improving the smoothness of the scanner's movement. The high-pressure resistance of the 3D laser scanner is achieved through a special structural design, using a high-pressure resistant sealing ring and a metal sealing shell to ensure that the scanner operates normally under high-pressure conditions.

[0089] Simultaneously, an in-situ charging module and a data transmission module are integrated within the scanner. The inspection path and scanning parameters are pre-set in the 3D laser scanner. The inspection path is configured according to the structural characteristics of the chamber and monitoring requirements, ensuring the scanner can perform a comprehensive scan of the chamber. After the inspection program is started, the scanner automatically slides along the track along the preset path, driven by a sliding component. During this sliding process, it performs a 3D laser scan of the internal structure of the chamber according to the set scanning parameters, acquiring 3D point cloud data of the chamber. Charging stations are set at specific locations on the track. When the 3D laser scanner's battery level falls below a preset threshold, the scanner automatically moves to the charging station and connects to the charging interface of the charging station via the in-situ charging module to begin charging. After charging is complete, the scanner automatically returns to the interrupted inspection path to continue the inspection work, without requiring manual intervention during the charging process. The data transmission module uses wireless transmission and selects a wireless communication protocol suitable for high-pressure enclosed environments to ensure the stability and reliability of data transmission. The inspection path can be flexibly adjusted according to actual monitoring needs, and the scanner's inspection path can be remotely modified via a monitoring terminal.

[0090] The monitoring terminal 502 is communicatively connected to the laser scanner 501 and is used to execute the deformation inspection method for the compressed air energy storage chamber described in the above embodiment. Exemplarily, during the scanning process, the 3D laser scanner uploads 3D point cloud data to the monitoring terminal outside the chamber in real time via a data transmission module. The monitoring terminal performs preprocessing on the uploaded point cloud data, including denoising, stitching, and registration, to generate a 3D model of the current state of the chamber, and extracts the coordinate data of the monitoring points to determine the inspection results. For details, please refer to the description of the relevant content in the above embodiment; it will not be repeated here.

[0091] The deformation inspection method for compressed air energy storage chambers provided in this application will be described below through a specific embodiment.

[0092] Example: A track, made of high-strength, pressure-resistant material, is laid along the inner wall of the compressed air chamber, running parallel to the chamber's length. A sliding assembly compatible with the 3D laser scanner is installed on the track to ensure smooth movement. The track is positioned to cover the inner wall of the chamber.

[0093] A high-pressure resistant 3D laser scanner is selected, with a pressure resistance level no lower than the maximum working pressure of the chamber. The 3D laser scanner is mounted on a sliding assembly on a track, allowing it to move freely along the track and cover the chamber walls with a scanning angle. Simultaneously, an in-situ charging module and a data transmission module are integrated within the scanner.

[0094] The inspection path and scanning parameters are pre-set in the 3D laser scanner. The inspection path is set according to the structural characteristics of the chamber and the monitoring requirements to ensure that the scanner can perform a comprehensive scan of the chamber. After the inspection program is started, the scanner automatically slides along the track according to the preset path under the drive of the sliding component. During the sliding process, it performs a 3D laser scan of the internal structure of the chamber according to the set scanning parameters to obtain the 3D point cloud data of the chamber.

[0095] Charging stations are set up at specific locations along the track. When the battery level of the 3D laser scanner falls below a preset threshold, the scanner automatically moves to the charging station and connects to its charging interface via its on-site charging module. Once charging is complete, the scanner automatically returns to its interrupted inspection path to continue its inspection work, without requiring manual intervention during the charging process.

[0096] During the scanning process, the 3D laser scanner uploads the 3D point cloud data to the monitoring terminal outside the tunnel in real time via the data transmission module. The monitoring terminal performs preprocessing on the uploaded point cloud data, including denoising, stitching, and registration, to generate a 3D model of the tunnel's current state and extract the coordinate data of key deformation monitoring points.

[0097] A risk assessment model is constructed based on preprocessed deformation data, and a risk assessment index system is set, including indicators such as deformation threshold, deformation rate, and deformation distribution uniformity. Deformation data from extracted key monitoring points are substituted into the risk assessment model to calculate risk assessment values. Risk levels (low, medium, and high) are classified according to the risk assessment values. When the assessment result is medium risk, the monitoring terminal issues an alert signal and pushes a deformation analysis report; when the assessment result is high risk, an emergency warning is immediately triggered, and emergency response suggestions are automatically generated. The deformation risk assessment model is constructed using the analytic hierarchy process (AHP) combined with fuzzy comprehensive evaluation. The AHP is used to determine the weights of each assessment index, while the fuzzy comprehensive evaluation method is used to conduct a multi-factor comprehensive assessment of deformation risk.

[0098] The method provided in this application eliminates the need for manual inspection, eliminating the need for personnel to enter the high-pressure chamber, thus reducing safety risks and improving inspection efficiency. By laying a track on the top of the chamber, a 3D laser scanner can automatically slide along the track for inspection, providing comprehensive scanning coverage and acquiring overall 3D point cloud data of the chamber. Compared to single-point sensor monitoring, the monitoring results are more comprehensive and accurate, enabling timely detection of overall chamber deformation. The 3D laser scanner is characterized by high-pressure resistance and on-site charging, allowing it to operate normally in the high-pressure environment of the compressed air artificial chamber, achieving autonomous charging and ensuring the continuity of inspection work, thereby improving inspection efficiency. The data transmission module can upload the scanned data to the monitoring terminal in real time, enabling real-time monitoring and early warning of chamber deformation, facilitating timely countermeasures and ensuring the safe operation of the chamber. By constructing a deformation risk assessment model, a quantitative assessment and classification of chamber deformation risks can be achieved, providing a scientific basis for chamber safety management and avoiding safety accidents or excessive maintenance due to inaccurate risk assessment.

[0099] This embodiment also provides a deformation inspection device for a compressed air energy storage chamber. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0100] This embodiment provides a deformation inspection device for a compressed air energy storage chamber, such as... Figure 6 As shown, it includes: The acquisition module 601 is used to acquire regional point cloud data of multiple key areas in the target chamber and the theoretical positions of monitoring points in each key area. Each key area contains multiple monitoring points, and the setting method of monitoring points in different key areas is different. The first determining module 602 is used to determine the local point cloud data of the corresponding monitoring point in the regional point cloud data, with the theoretical position of each monitoring point as the center and the target value as the radius. The second determining module 603 is used to determine the centroid position based on the local point cloud data of each monitoring point; Calculation module 604 is used to calculate the global transformation matrix based on the centroid position and theoretical position corresponding to different monitoring points; The calibration module 605 is used to calibrate the local point cloud data of each key area based on the global transformation matrix to obtain the absolute position of each monitoring point after calibration. The third determining module 606 is used to determine the deformation inspection results of the corresponding key area based on the absolute position of multiple monitoring points in each key area and the historical inspection positions obtained in advance.

[0101] In some alternative implementations, the acquisition module 601 includes: The acquisition submodule is used to acquire discrete 3D point cloud data, geometric parameters of the target chamber, and location information of multiple key areas in the target chamber. The discrete 3D point cloud data is obtained by scanning the inner wall of the target chamber using a 3D laser scanner. The discrete 3D point cloud data is used to characterize the 3D point cloud data at different scanning positions. The stitching submodule is used to stitch discrete 3D point cloud data based on geometric parameters to obtain the overall point cloud data of the target chamber; The first determination submodule is used to determine the regional point cloud data of the corresponding key regions in the overall point cloud data based on the location information of each key region.

[0102] In some alternative implementations, the splicing submodule includes: The processing unit is used to denoise the discrete 3D point cloud data to obtain effective discrete point cloud data; The first determining unit is used to determine the design geometric features of the target chamber based on geometric parameters; The extraction unit is used to extract point cloud geometric feature information corresponding to geometric parameters from effective discrete point cloud data; The matching unit is used to match the point cloud geometric feature information with the design geometric features to obtain the matching result; The stitching unit is used to stitch together discrete three-dimensional point cloud data based on the matching results to obtain the overall point cloud data of the target chamber.

[0103] In some alternative implementations, the third determining module 606 includes: The second determination submodule is used to determine the deformation of the corresponding monitoring point based on the absolute position of each monitoring point and the historical inspection position. The third determination submodule is used to determine the deformation rate of the corresponding monitoring point based on the deformation of each monitoring point and the inspection interval time. The fourth determination submodule is used to determine the deformation distribution uniformity index value of the corresponding key area based on the deformation of multiple monitoring points in each key area; The fifth determination submodule is used to determine the deformation inspection results of the corresponding key areas based on the deformation, deformation rate and deformation distribution uniformity index values ​​of multiple monitoring points in each key area.

[0104] In some alternative implementations, the fifth determining submodule includes: The acquisition unit is used to acquire indicator weight information; The second determining unit is used to determine the risk assessment value of the corresponding key area based on the deformation, deformation rate, deformation distribution uniformity index value and index weight information of multiple monitoring points in each key area. The third determining unit is used to determine the deformation inspection results of the corresponding key areas based on the risk assessment values ​​of each key area.

[0105] The deformation inspection device for compressed air energy storage chambers provided in this embodiment of the invention can execute the deformation inspection method for compressed air energy storage chambers provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0106] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0107] The following is a detailed reference. Figure 7 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0108] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0109] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the deformation inspection method for compressed air energy storage chambers according to embodiments of the present invention.

[0110] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0111] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the deformation inspection method for the compressed air energy storage chamber shown in the above embodiments is implemented.

[0112] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0113] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for deformation inspection of a compressed air energy storage chamber, characterized in that, The method includes: Acquire regional point cloud data of multiple key areas in the target chamber and the theoretical positions of monitoring points in each key area. Each key area contains multiple monitoring points, and the setting method of monitoring points in different key areas is different. Using the theoretical location of each monitoring point as the center and the target value as the radius, determine the local point cloud data of the corresponding monitoring point in the point cloud data of the area; The centroid position is determined based on the local point cloud data of each monitoring point; Calculate the global transformation matrix based on the centroid position and the theoretical position corresponding to different monitoring points; Based on the global transformation matrix, the local point cloud data of each key area are calibrated to obtain the calibrated absolute position of each monitoring point. The deformation inspection results for the corresponding key areas are determined based on the absolute positions of multiple monitoring points within each key area and the previously acquired historical inspection positions.

2. The method according to claim 1, characterized in that, The steps of acquiring local point cloud data of multiple key areas in the target chamber and the theoretical positions of monitoring points in each key area include: The discrete three-dimensional point cloud data of the target chamber, the geometric parameters of the target chamber, the location information of multiple key areas, and the theoretical location of monitoring points in each key area are obtained. The discrete three-dimensional point cloud data is obtained by scanning the inner wall of the target chamber using a three-dimensional laser scanner. The discrete three-dimensional point cloud data is used to characterize the three-dimensional point cloud data at different scanning positions. Based on the geometric parameters, the discrete three-dimensional point cloud data is stitched together to obtain the overall point cloud data of the target chamber; Based on the location information of each key region, the corresponding regional point cloud data of the key region is determined in the overall point cloud data.

3. The method according to claim 2, characterized in that, Based on the geometric parameters, the discrete three-dimensional point cloud data is stitched together to obtain the overall point cloud data of the target chamber, including: The discrete 3D point cloud data is denoised to obtain effective discrete point cloud data; The design geometric features of the target chamber are determined based on the aforementioned geometric parameters; Extract point cloud geometric feature information corresponding to the geometric parameters from the effective discrete point cloud data; The point cloud geometric feature information is matched with the design geometric features to obtain the matching result; Based on the matching results, the discrete three-dimensional point cloud data are stitched together to obtain the overall point cloud data of the target chamber.

4. The method according to any one of claims 1 to 3, characterized in that, The step of determining the deformation inspection results of the corresponding key area based on the absolute positions of multiple monitoring points within each key area and the pre-acquired historical inspection positions includes: The deformation of the corresponding monitoring point is determined based on the absolute position of each monitoring point and the historical inspection position. The deformation rate of the corresponding monitoring point is determined based on the deformation of each monitoring point and the inspection interval. The deformation distribution uniformity index value of the corresponding key area is determined based on the deformation of multiple monitoring points in each key area; Based on the deformation, deformation rate, and deformation distribution uniformity index values ​​of multiple monitoring points in each key area, the deformation inspection results of the corresponding key area are determined.

5. The method according to claim 4, characterized in that, The step of determining the deformation inspection results of the corresponding key areas based on the deformation amount, deformation rate, and deformation distribution uniformity index value of multiple monitoring points in each key area includes: Obtain indicator weight information; Based on the deformation, deformation rate, deformation distribution uniformity index value, and index weight information of multiple monitoring points in each key area, the risk assessment value of the corresponding key area is determined. The deformation inspection results for each key area are determined based on the risk assessment values ​​of each key area.

6. A deformation inspection system for a compressed air energy storage chamber, characterized in that, The system includes: a laser scanner and a monitoring terminal; The laser scanner is connected to the inspection track in the target chamber via a sliding assembly, and is used to slide along the inspection track under the drive of the sliding assembly, and collect three-dimensional point cloud data inside the target chamber during the sliding process; The monitoring terminal is communicatively connected to the laser scanner and is used to perform the deformation inspection method of the compressed air energy storage chamber according to any one of claims 1 to 4.

7. A deformation inspection device for a compressed air energy storage chamber, characterized in that, The device includes: The acquisition module is used to acquire regional point cloud data of multiple key areas in the target chamber and the theoretical positions of monitoring points in each key area. Each key area contains multiple monitoring points, and the setting method of monitoring points in different key areas is different. The first determining module is used to determine the local point cloud data of the corresponding monitoring point in the regional point cloud data, with the theoretical position of each monitoring point as the center and the target value as the radius. The second determining module is used to determine the centroid position based on the local point cloud data of each monitoring point; The calculation module is used to calculate the global transformation matrix based on the centroid position and the theoretical position corresponding to different monitoring points; The calibration module is used to calibrate the local point cloud data of each key area based on the global transformation matrix to obtain the calibrated absolute position of each monitoring point. The third determining module is used to determine the deformation inspection results of the corresponding key area based on the absolute positions of multiple monitoring points in each key area and the pre-acquired historical inspection positions.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the deformation inspection method for the compressed air energy storage chamber as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the deformation inspection method for the compressed air energy storage chamber according to any one of claims 1 to 5.

10. A computer program product, characterized in that, Includes computer instructions, which are used to cause a computer to execute the deformation inspection method for the compressed air energy storage chamber according to any one of claims 1 to 5.

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