A structural fatigue loading laboratory high-risk area warning method and system

Abstract

This invention discloses a method and system for warning high-risk areas in a structural fatigue loading laboratory. The method includes: configuring an equal number of cameras based on the number of sides of the main dimensions of the experimental equipment; establishing a warning zone around the dangerous moving parts of the equipment, completing its digital calibration, and simultaneously digitally storing the monitoring areas of each camera to ensure comprehensive monitoring of the equipment and the warning zone; acquiring the equipment's operating status and activating monitoring only when the equipment is running; comparing the timestamps of different cameras capturing "personnel entering the warning zone" to complete time consistency verification; mapping the pixel coordinates of each camera to a unified physical coordinate system and comparing the personnel positions to complete spatial consistency verification, with only the verified data used for risk assessment; determining whether there are personnel active in the warning zone near the equipment, feeding back to the control center, and sending a signal to the alarm system. This improves the safety protection level of the fatigue loading laboratory.

Description

Technical Field

[0001] This invention belongs to the field of warning technology for loading experiments, and more specifically, relates to a method and system for warning of high-risk areas in a structural fatigue loading laboratory. Background Technology

[0002] During long-term operation, bridge structures are continuously subjected to complex and variable external loads. The dynamic impact of vehicle loads, the alternating effects of strong wind loads, and the erosion of environmental corrosive media constitute the main causes of structural fatigue damage. These alternating loads repeatedly act on critical bridge components (such as bearings, connection nodes, and steel box girders), gradually initiating and propagating micro-cracks in the materials, which may ultimately lead to sudden structural failure, posing a serious threat to operational safety. Numerous bridge structural accidents both domestically and internationally are directly related to fatigue damage. Therefore, accurately assessing the impact of fatigue loads and fatigue-corrosion coupling on structural lifespan has become a core issue urgently needing to be addressed in the field of bridge engineering.

[0003] Due to the complexity of actual service environments, theoretical analysis or numerical simulation alone cannot accurately reproduce the damage evolution of structures under alternating loads and corrosive environments. Theoretical models often simplify load spectrum characteristics and material degradation mechanisms, while the accuracy of numerical simulations is limited by constitutive assumptions and boundary condition settings, leading to significant deviations between theoretical results and the fatigue performance of actual structures. Therefore, universities, research institutions, and engineering testing units commonly use specialized equipment such as fatigue testing machines and electro-hydraulic servo loading systems to conduct fatigue loading tests on full-scale or scaled-down specimens to obtain fatigue life data and damage evolution patterns that more closely reflect reality.

[0004] These loading devices simulate the cyclic loads experienced by bridge structures. Their core moving parts (such as hydraulic pistons, eccentric wheels, and clamping devices) need to be in a state of high-frequency reciprocating motion for extended periods, and the loading force typically reaches a high level. If these moving parts collide with, crush, or become entangled in personnel, they will instantly generate an impact force far exceeding the limits of human endurance, leading to fatal consequences. Currently, laboratory safety management mainly relies on static measures: conducting safety training before operations to clarify operating procedures; demarcating high-risk areas with warning lines and protective fences to restrict entry by non-operating personnel; and posting warning signs next to the equipment to remind users of precautions.

[0005] However, these measures have significant limitations: firstly, static protection cannot address emergencies such as personnel carelessly crossing warning lines or accidentally touching equipment; secondly, over-reliance on the subjective safety awareness of operators makes it difficult to prevent human errors such as fatigue and violations of operating procedures; and thirdly, when potential risks such as abnormal vibrations or moving parts shift during equipment operation occur, there is a lack of real-time monitoring and early warning capabilities, easily missing the optimal intervention opportunity. Therefore, constructing an intelligent control system capable of proactively identifying personnel intrusion, monitoring equipment status in real time, and dynamically issuing warnings is of great significance for compensating for the shortcomings of traditional measures and improving the safety protection level of fatigue loading laboratories. Summary of the Invention

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a method for warning of high-risk areas in a structural fatigue loading laboratory, comprising:

[0007] S1. Configure the same number of cameras according to the number of sides of the main dimensions of the experimental equipment;

[0008] S2. Set up a warning zone around the moving parts of the equipment where there is a danger; at the same time, complete the digital calibration of the warning zone; digitally store the monitoring area of ​​each camera; the monitoring areas of each camera together constitute a blind-spot-free monitoring of the equipment and the warning zone;

[0009] S3. Complete the acquisition of the operating status of the monitored device; monitoring is only enabled when the monitored device is in operation.

[0010] S4. The temporal consistency of the early warning data is verified by comparing the timestamps of "personnel entering the warning zone" captured by different cameras; the pixel coordinates of each camera are mapped to a unified physical coordinate system through coordinate transformation, and the position of personnel in different camera images is compared to verify the spatial consistency of the early warning data; only data that passes the spatiotemporal consistency verification is used as valid input for risk assessment.

[0011] S5. Determine whether there are any test personnel active in the warning area near the monitored equipment, and report back to the control center, and send an alarm signal to the alarm system.

[0012] Furthermore, in S2, the warning area setting is based on the moving parts of the equipment where there is a danger, and the boundary is defined around it within a range of 1 to 1.5 meters by identifiable markers.

[0013] Furthermore, the specific method for digitally labeling the warning area in S2 is as follows:

[0014] Walk around the boundary of the warning line area with a ruler in hand, and stand the ruler upright at the corner. Collect and save image data from the camera position. Analyze the collected images and determine the warning area in the picture based on the area enclosed by the ruler's trajectory at the corner point. The length of the ruler shall not be less than 2 meters.

[0015] Furthermore, the specific process of analyzing the acquired images is as follows:

[0016] Let the actual length of the scale be... The corresponding pixel length in the image is Then the pixel-to-physical scale conversion factor for:

[0017]

[0018] Identify the pixel coordinates of the scale in the image at the corner of the warning zone boundary, and calculate the pixel distance between adjacent corner points in the image. :

[0019]

[0020] in, The pixel coordinates of adjacent corner points are used; the actual physical distance between these adjacent corner points is obtained through on-site measurement. Then calculate the conversion factor for this segment. :

[0021]

[0022] contrast and If the deviation between the two is within a preset threshold, the average value is taken. This serves as the final scale conversion factor. If the deviation exceeds the limit, re-check the scale placement and image acquisition process, eliminate errors, and recalculate.

[0023] Traverse all corner point pixel coordinates Use the verified Calculate the actual distance between each adjacent point and fit the boundary of the warning zone; generate pixel polygons of the warning zone in the image using polygon fitting method, convert them into recognizable digital boundary parameters, and complete the calibration.

[0024] Furthermore, the method for obtaining the operating status of the monitored device in S3 is specifically as follows:

[0025] If the control system of the monitored equipment provides an open interface, the equipment's operating status signal is read directly; if there is no corresponding interface, feature patterns are affixed to the moving parts of the equipment where there is a risk, and video analysis methods are used to determine whether the equipment is in motion.

[0026] Furthermore, the time consistency check in S4 specifically includes:

[0027] set up The timestamp of each camera capturing "personnel entering the restricted area" is... Then the standard deviation of the timestamp set for:

[0028]

[0029] in, The average of timestamps;

[0030] Maximum deviation rate for:

[0031]

[0032] The initial threshold value is set based on the camera frame rate and the network latency limit (e.g., 50ms). ;like If so, the threshold will be lowered according to the set standard; if The threshold is adjusted upwards according to the set standard, ultimately forming a dynamic threshold. If a single timestamp exists satisfy If the outlier is removed, the calculation is recalculated; at the same time, a time consistency confidence level is defined. :

[0033]

[0034] When satisfied If the time is consistent, then the time consistency is determined to be consistent;

[0035] At the same time, the confidence level of time consistency needs to be further examined. ,when When, it is judged as "high confidence consistency"; when When, it is judged as "low confidence consistency"; when If so, it is judged as "inconsistent".

[0036] Furthermore, the spatial consistency check in S4 specifically includes:

[0037] set up The set of physical installation location coordinates of the cameras is For each camera, establish a mapping relationship from pixel coordinates to physical coordinates using camera intrinsic and extrinsic parameters:

[0038] If the pixel coordinates of the "person enters the restricted area" event in a certain camera are Then the corresponding physical space coordinates satisfy:

[0039] Based on the above mapping, calculate The physical space coordinates of the same event corresponding to each camera are: ;

[0040] Calculate the coordinates and spatial mean points 3D Euclidean distance:

[0041]

[0042] Define spatial discreteness :

[0043]

[0044] The basic spatial threshold is set based on the overlap of the camera's field of view and the calibration accuracy. ;like If so, the threshold will be lowered according to the set standard; if The threshold setting standard has been adjusted upwards, forming a dynamic threshold. ; Calculate the maximum spatial deviation :

[0045]

[0046] If a certain coordinate satisfy If so, remove it and recalculate;

[0047] Compute spatial consistency confidence :

[0048]

[0049] When satisfied And the minimum bounding sphere radius of the set of spatial coordinate points If the spatial consistency requirement is met, then the determination is made based on the confidence level of spatial consistency: when When, it is "high confidence spatial consistency"; when When, it is "low confidence consistency"; when If so, it is determined as "spatial inconsistency".

[0050] Furthermore, in S5, test personnel must wear safety helmets, reflective vests, or uniforms when entering the test area.

[0051] As a second aspect of the present invention, the present invention provides a high-risk area warning system for a structural fatigue loading laboratory, comprising:

[0052] A camera configuration unit is used to configure the same number of cameras according to the number of sides of the main scale of the experimental equipment;

[0053] The warning zone setting unit is used to set up warning zones around the moving parts of the equipment where there is a danger; at the same time, it completes the digital calibration of the warning zones; digitally stores the monitoring areas of each camera; and the monitoring areas of each camera together constitute a blind-spot-free monitoring of the equipment and the warning zones.

[0054] The monitoring status activation unit is used to acquire the operating status of the monitored device; monitoring is only activated when the monitored device is in operation.

[0055] The spatiotemporal consistency verification unit is used to verify the temporal consistency of the warning data by comparing the timestamps of "personnel entering the warning zone" captured by different cameras; and to map the pixel coordinates of each camera to a unified physical coordinate system by coordinate transformation, and to compare the position of personnel in the images of different cameras to complete the spatial consistency verification of the warning data; only the data that passes the spatiotemporal consistency verification is used as valid input for risk assessment.

[0056] The judgment and alarm unit is used to determine whether there are test personnel active in the warning area near the monitored equipment, and to report back to the control center and send an alarm signal to the alarm system.

[0057] As a third aspect of the invention, the invention provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of any step of the aforementioned method for warning of high-risk areas in a structural fatigue loading laboratory.

[0058] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0059] 1. The high-risk area warning method for structural fatigue loading laboratories of the present invention configures an equal number of cameras according to the number of sides of the main dimensions of the experimental equipment, sets up a warning zone around the dangerous moving parts and completes digital calibration, and simultaneously digitally stores the monitoring area of ​​each camera to ensure comprehensive monitoring without blind spots. This technical feature, through multi-camera collaborative coverage and digital spatial definition, accurately locks the spatial range of high-risk areas, avoids missed risk detection due to monitoring blind spots, and ensures that the dynamic changes of the equipment and warning zone are under real-time monitoring, providing comprehensive spatial data support for subsequent risk identification.

[0060] 2. The high-risk area warning method for structural fatigue loading laboratories of the present invention acquires the equipment's operating status and only activates monitoring during operation. It combines timestamp comparison to verify the temporal consistency of the warning data and maps pixel coordinates to a unified physical coordinate system to verify spatial consistency. Only data that passes both verifications is used for risk assessment. This technical feature, through a device status-linked monitoring strategy and spatiotemporal dual-dimensional verification, effectively filters invalid monitoring data from non-operating states and false alarms from single cameras, improving the accuracy and reliability of warning data and reducing false alarms caused by environmental interference or equipment idleness. Attached Figure Description

[0061] Figure 1 This is a flowchart of the high-risk area warning method for structural fatigue loading in a laboratory according to an embodiment of the present invention;

[0062] Figure 2 This is an example diagram of a rectangular arrangement of device cameras according to an embodiment of the present invention;

[0063] Figure 3 This is an example diagram of a rectangular arrangement of device cameras according to an embodiment of the present invention;

[0064] Figure 4 This is an example diagram illustrating the calibration video analysis range according to an embodiment of the present invention;

[0065] Figure 5 This is a system unit diagram of an embodiment of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0067] Example 1

[0068] Please refer to Figure 1 This embodiment 1 provides a high-risk area warning method for structural fatigue loading laboratories, including:

[0069] S1. Configure the same number of cameras according to the number of sides of the main dimensions of the experimental equipment;

[0070] S2. Set up a warning zone around the moving parts of the equipment where there is a danger; at the same time, complete the digital calibration of the warning zone; digitally store the monitoring area of ​​each camera; the monitoring areas of each camera together constitute a blind-spot-free monitoring of the equipment and the warning zone;

[0071] S3. Complete the acquisition of the operating status of the monitored device; monitoring is only enabled when the monitored device is in operation.

[0072] S4. The temporal consistency of the early warning data is verified by comparing the timestamps of "personnel entering the warning zone" captured by different cameras; the pixel coordinates of each camera are mapped to a unified physical coordinate system through coordinate transformation, and the position of personnel in different camera images is compared to verify the spatial consistency of the early warning data; only data that passes the spatiotemporal consistency verification is used as valid input for risk assessment.

[0073] S5. Determine whether there are any test personnel active in the warning area near the monitored equipment, and report back to the control center, and send an alarm signal to the alarm system.

[0074] This embodiment 1 further elaborates on the above steps.

[0075] (1) Camera configuration

[0076] Please refer to Figure 2 as well as Figure 3 To ensure effective monitoring of experimental equipment and surrounding high-risk areas, the camera configuration must be consistent with the number of sides of the equipment's main dimensions. The core purpose of this configuration is to avoid misjudgments and false alarms regarding the location of personnel or objects. By acquiring images from different sides of the equipment at multiple angles, cross-validation of multi-view data can be achieved, thereby improving the accuracy of location determination. Typical configuration schemes can be found in the corresponding illustrations.

[0077] When arranging the equipment, adjustments should be made flexibly based on the actual characteristics of the site. If multiple devices to be monitored are close together, and there are high-rise buildings or other suitable high-position installation points within the site, the lens focal length and installation angle of the cameras can be adjusted appropriately to allow a single camera to cover multiple devices simultaneously, optimizing equipment configuration while ensuring monitoring effectiveness. If the devices are scattered, or if there are obstructions affecting the field of view, a separate set of cameras should be configured for each device to ensure that the surveillance area of ​​each device is completely captured, avoiding blind spots.

[0078] (2) Setting up a warning zone

[0079] A warning zone is established around the dangerous moving parts of the equipment. The warning zone is set with the dangerous moving parts of the equipment as the core. The boundary of the warning zone is marked within a range of 1 to 1.5 meters around it by identifiable markers. This is the basic line of defense for building safety protection, which can clearly define the boundary of high-risk areas and provide clear physical reference for subsequent monitoring.

[0080] Please refer to Figure 4Then, images are collected along the boundary of the warning zone using a handheld ruler and digital calibration is completed. The conversion relationship between pixels and physical scale is established with the help of the ruler. After multi-dimensional verification to ensure the accuracy of the conversion, digital boundary parameters are generated by fitting. This process transforms the physical warning zone into digital information that the system can recognize, providing a unified digital standard for subsequent accurate judgment of whether personnel have entered the warning zone, and avoiding judgment errors caused by differences in spatial cognition.

[0081] In a preferred embodiment, the specific method for digitally marking the warning area is as follows:

[0082] Walk around the boundary of the warning line area with a ruler in hand, and stand the ruler upright at the corner. Collect and save image data from the camera position. Analyze the collected images and determine the warning area in the picture based on the area enclosed by the ruler's trajectory at the corner point. The length of the ruler shall not be less than 2 meters.

[0083] In a preferred embodiment, the specific process of analyzing the acquired images is as follows:

[0084] Let the actual length of the scale be... The corresponding pixel length in the image is Then the pixel-to-physical scale conversion factor for:

[0085]

[0086] Identify the pixel coordinates of the scale in the image at the corner of the warning zone boundary, and calculate the pixel distance between adjacent corner points in the image. :

[0087]

[0088] in, The pixel coordinates of adjacent corner points are used; the actual physical distance between these adjacent corner points is obtained through on-site measurement. Then calculate the conversion factor for this segment. :

[0089]

[0090] contrast and If the deviation between the two is within a preset threshold, the average value is taken. This serves as the final scale conversion factor. If the deviation exceeds the limit, re-check the scale placement and image acquisition process, eliminate errors, and recalculate.

[0091] Traverse all corner point pixel coordinates Use the verified Calculate the actual distance between each adjacent point and fit the boundary of the warning zone; generate pixel polygons of the warning zone in the image using polygon fitting method, convert them into recognizable digital boundary parameters, and complete the calibration.

[0092] Simultaneously, the monitoring areas of each camera need to be digitally stored. When deploying cameras, ensure there are no blind spots in the area. After determining their positions, use rigid connections to fix the cameras to walls, columns, or other structures to prevent them from shifting during operation. Once the monitoring areas of each camera are digitally stored, they collectively form a comprehensive, blind-spot-free monitoring system for the equipment and the monitored area.

[0093] (3) Monitoring status enabled

[0094] To achieve accurate perception and judgment of the operating status of monitored equipment, it is necessary to select an appropriate status acquisition method based on the equipment's interface conditions. When the equipment control system has an open interface, its operating status signal can be read directly. This method can directly obtain the real-time operating parameters of the equipment, ensuring the accuracy and timeliness of status judgment and providing the most direct basis for starting the monitoring system. If the equipment does not have an open interface, indirect judgment can be made by affixing characteristic patterns to moving parts that may pose a risk—specific paint patterns, reflective stripes, or reflective mirrors are selected as identification markers. These markers can form significant visual features in the video image. By continuously tracking the position changes and movement trajectories of the markers through video analysis programs, it can be determined whether the moving parts are in motion, and thus whether the equipment is operating.

[0095] Simultaneously, monitoring of the corresponding warning area is only activated when the monitored equipment is in operation. The core of this strategy is to achieve precise linkage between monitoring and equipment risk status. When the equipment is stationary, even if personnel are active near the warning area, there is no need to activate high-intensity monitoring because the moving parts pose no risk. However, when the equipment is running, the moving parts are in a state of high-frequency movement, and the risk factor is significantly increased. At this time, activating monitoring can concentrate system resources to focus on monitoring the warning area.

[0096] This dynamic control method can not only effectively reduce false alarms caused by environmental interference or unauthorized personnel movement when equipment is idle, avoiding unnecessary interference with normal laboratory operations, but also improve the system's response efficiency to real risks by focusing on monitoring high-risk periods, ensuring timely detection and early warning when dangerous situations occur.

[0097] (4) Spatiotemporal consistency verification

[0098] After completing the monitoring of equipment operation status and the digital calibration of the warning area, to ensure the reliability of the monitoring data, it is necessary to perform dual consistency verification in both time and space on the "personnel entering the warning area" information captured by each camera. By comparing the timestamps recorded by different cameras and dynamically evaluating the temporal correlation, and by mapping the pixel coordinates of each viewpoint to a unified physical coordinate system to verify the spatial location matching degree, only the data that passes the dual verification is retained as valid input for risk assessment. This step not only accurately filters the information collected at the front end, but also lays a solid data foundation for the subsequent accurate identification of dangerous scenarios.

[0099] Time consistency verification compares the timestamps of "personnel entering the restricted area" captured by each camera, calculates their concentration and dispersion, combines a dynamic baseline threshold with dynamic adjustments, and incorporates outlier removal mechanisms and confidence assessments to determine whether the time data is consistent. This process effectively filters out time deviations caused by network latency and differences in device response, ensuring that events recorded from different perspectives are correlated in the time dimension and avoiding misjudgments caused by errors in a single time recording.

[0100] In a preferred embodiment, the time consistency check specifically involves:

[0101] set up The timestamp of each camera capturing "personnel entering the restricted area" is... Then the standard deviation of the timestamp set for:

[0102]

[0103] in, The average of timestamps;

[0104] Maximum deviation rate for:

[0105]

[0106] The initial threshold value is set based on the camera frame rate and the network latency limit (e.g., 50ms). ;like If so, the threshold will be lowered according to the set standard; if The threshold is adjusted upwards according to the set standard, ultimately forming a dynamic threshold. If a single timestamp exists satisfy If the outlier is removed, the calculation is recalculated; at the same time, a time consistency confidence level is defined. :

[0107]

[0108] When satisfied If the time is consistent, then the time consistency is determined to be consistent;

[0109] At the same time, the confidence level of time consistency needs to be further examined. ,when When, it is judged as "high confidence consistency"; when When, it is judged as "low confidence consistency"; when If so, it is judged as "inconsistent".

[0110] Spatial consistency verification transforms the pixel coordinates of each camera to a unified physical coordinate system. By calculating the dispersion and deviation range of coordinates from different viewpoints, and combining this with dynamically adjusted spatial thresholds and confidence assessments, it determines whether the spatial positions are consistent. This method, through cross-verification of spatial positions from multiple viewpoints, eliminates positional judgment errors caused by differences in camera installation angles and fields of view, ensuring accurate and reliable spatial positioning of personnel entering the restricted area.

[0111] In a preferred embodiment, spatial consistency verification specifically involves:

[0112] set up The set of physical installation location coordinates of the cameras is For each camera, establish a mapping relationship from pixel coordinates to physical coordinates using camera intrinsic and extrinsic parameters:

[0113] If the pixel coordinates of the "person enters the restricted area" event in a certain camera are Then the corresponding physical space coordinates satisfy:

[0114] Based on the above mapping, calculate The physical space coordinates of the same event corresponding to each camera are: ;

[0115] Calculate the coordinates and spatial mean points 3D Euclidean distance:

[0116]

[0117] Define spatial discreteness :

[0118]

[0119] The basic spatial threshold is set based on the overlap of the camera's field of view and the calibration accuracy. ;like If so, the threshold will be lowered according to the set standard; if The threshold setting standard has been adjusted upwards, forming a dynamic threshold. ; Calculate the maximum spatial deviation :

[0120]

[0121] If a certain coordinate satisfy If so, remove it and recalculate;

[0122] Compute spatial consistency confidence :

[0123]

[0124] When satisfied And the minimum bounding sphere radius of the set of spatial coordinate points If the spatial consistency requirement is met, then the determination is made based on the confidence level of spatial consistency: when When, it is "high confidence spatial consistency"; when When, it is "low confidence consistency"; when If so, it is determined as "spatial inconsistency".

[0125] By using only data that has undergone dual verification for risk assessment, this mechanism significantly reduces the risk of false alarms caused by factors such as single device failure and environmental interference through collaborative verification in time and space dimensions. This makes the data input into the risk assessment system more authentic and reliable, providing a solid data foundation for subsequent accurate identification of dangerous scenarios and timely warnings, and improving the decision-making accuracy and response effectiveness of the entire monitoring system.

[0126] (5) Judgment and warning

[0127] When potentially hazardous equipment is in operation, the surveillance video transmitted by the cameras is analyzed. The video analysis primarily focuses on items resembling laboratory personnel, such as safety helmets, reflective vests, and work clothes. Where possible, all human bodies are also captured and identified to comprehensively cover any potential personnel activity. To avoid false alarms, for any monitored device, only when the analysis results from all cameras monitoring it indicate the presence of personnel within the monitored area (i.e., the warning zone) is it determined that someone is active near that device within the warning zone. This determination is then relayed to the control center, triggering an alarm system to issue a signal.

[0128] The alarm system can employ various alarm methods, including buzzers, voice alerts, and warning lights. Specifically, one approach is to place audible and visual alarm devices near each monitored device, activating only the alarm device corresponding to the device that is running and has personnel approaching nearby, thus achieving precise alarm detection. Another approach is to share a single alarm system within the laboratory, using voice announcements to clearly inform relevant personnel which running device has personnel approaching, ensuring that they can quickly pinpoint the risk location.

[0129] To ensure the smooth operation of the system, appropriate laboratory management measures are also required. Personnel entering the testing area must wear safety helmets, reflective vests, or uniforms as required to facilitate accurate identification by the video analysis program; laboratory personnel should receive pre-job training to fully understand the meaning of various warning signals so as to react promptly upon receiving an alarm; cameras need to be inspected regularly to ensure they are working properly and that the shooting angle is not off, ensuring the effectiveness of the monitoring footage; at the same time, the system's effectiveness should be tested regularly. Under the premise of ensuring safety, personnel should be simulated to enter the warning area of ​​the operating testing equipment to verify whether the system can trigger alarms normally, thereby ensuring the continuous and reliable operation of the entire system.

[0130] Example 2

[0131] Please refer to Figure 5 This embodiment 2 provides a high-risk area warning system for a structural fatigue loading laboratory, including:

[0132] A camera configuration unit is used to configure the same number of cameras according to the number of sides of the main scale of the experimental equipment;

[0133] The warning zone setting unit is used to set up warning zones around the moving parts of the equipment where there is a danger; at the same time, it completes the digital calibration of the warning zones; digitally stores the monitoring areas of each camera; and the monitoring areas of each camera together constitute a blind-spot-free monitoring of the equipment and the warning zones.

[0134] The monitoring status activation unit is used to acquire the operating status of the monitored device; monitoring is only activated when the monitored device is in operation.

[0135] The spatiotemporal consistency verification unit is used to verify the temporal consistency of the warning data by comparing the timestamps of "personnel entering the warning zone" captured by different cameras; and to map the pixel coordinates of each camera to a unified physical coordinate system by coordinate transformation, and to compare the position of personnel in the images of different cameras to complete the spatial consistency verification of the warning data; only the data that passes the spatiotemporal consistency verification is used as valid input for risk assessment.

[0136] The judgment and alarm unit is used to determine whether there are test personnel active in the warning area near the monitored equipment, and to report back to the control center and send an alarm signal to the alarm system.

[0137] Example 3

[0138] This embodiment 3 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement any step of a structural fatigue loading laboratory high-risk area warning method.

[0139] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0141] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for alerting high-risk areas in a structural fatigue loading laboratory, characterized by, include: S1. Configure the same number of cameras according to the number of sides of the main dimensions of the experimental equipment; S2. Establish a warning zone around the moving parts of the equipment where there is a danger; and simultaneously complete the digital calibration of the warning zone; The monitoring areas of each camera are digitally stored; the monitoring areas of each camera together constitute a comprehensive monitoring of the equipment and the guarded area without blind spots. S3. Complete the acquisition of the operating status of the monitored device; monitoring is only enabled when the monitored device is in operation. S4. The temporal consistency of the early warning data is verified by comparing the timestamps of "personnel entering the warning zone" captured by different cameras; the pixel coordinates of each camera are mapped to a unified physical coordinate system through coordinate transformation, and the position of personnel in different camera images is compared to verify the spatial consistency of the early warning data; only data that passes the spatiotemporal consistency verification is used as valid input for risk assessment. S5. Determine whether there are any test personnel active in the warning area near the monitored equipment, and report back to the control center, and send an alarm signal to the alarm system; The time consistency check in S4 specifically involves: set up The timestamp of each camera capturing "personnel entering the restricted area" is... Then the standard deviation of the timestamp set for: in, The average of timestamps; Maximum deviation rate for: The initial base threshold is set based on the camera frame rate and network latency limit. ;like If so, the threshold will be lowered according to the set standard; if The threshold is adjusted upwards according to the set standard, ultimately forming a dynamic threshold. If a single timestamp exists satisfy If outliers are removed, the calculation is recalculated; at the same time, a time consistency confidence level is defined. : When satisfied If the time is consistent, then the time consistency is determined to be consistent; At the same time, the confidence level of time consistency needs to be further examined. ,when When, it is judged as "high confidence consistency"; when When, it is judged as "low confidence consistency"; when If so, it is judged as "inconsistent"; The spatial consistency check in S4 specifically involves: set up The set of physical installation location coordinates of the cameras is For each camera, establish a mapping relationship from pixel coordinates to physical coordinates using camera intrinsic and extrinsic parameters: If the pixel coordinates of the "person enters the restricted area" event in a certain camera are Then the corresponding physical space coordinates satisfy: Based on the mapping relationship, calculate The physical space coordinates of the same event corresponding to each camera are: ; Calculate the coordinates and spatial mean points 3D Euclidean distance: Define spatial discreteness : The basic spatial threshold is set based on the overlap of the camera's field of view and the calibration accuracy. ;like If so, the threshold will be lowered according to the set standard; if The threshold setting standard has been adjusted upwards, forming a dynamic threshold. ; Calculate the maximum spatial deviation : If a certain coordinate satisfy If so, remove it and recalculate; Compute spatial consistency confidence : When satisfied And the minimum bounding sphere radius of the set of spatial coordinate points If the spatial consistency requirement is met, then the determination is made based on the confidence level of spatial consistency: when When, it is "high confidence spatial consistency"; when When, it is "low confidence consistency"; when If so, it is determined as "spatial inconsistency".

2. The method for warning of high-risk areas in a structural fatigue loading laboratory according to claim 1, characterized in that, The warning zone in S2 is set with the moving parts of the equipment that pose a danger as the core, and the boundary is defined around it within a range of 1 to 1.5 meters by identifiable markers.

3. The method for warning of high-risk areas in a structural fatigue loading laboratory according to claim 1, characterized in that, The specific method for digitally defining the warning area in S2 is as follows: Walk around the boundary of the warning line area with a ruler in hand, and stand the ruler upright at the corner. Collect and save image data from the camera position. Analyze the collected images and determine the warning area in the picture based on the area enclosed by the ruler's trajectory at the corner point. The length of the ruler shall not be less than 2 meters.

4. The method for warning of high-risk areas in a structural fatigue loading laboratory according to claim 3, characterized in that, The specific process of analyzing the acquired images is as follows: Let the actual length of the scale be... The corresponding pixel length in the image is Then the pixel-to-physical scale conversion factor for: Identify the pixel coordinates of the scale in the image at the corner of the warning zone boundary, and calculate the pixel distance between adjacent corner points in the image. : in, The pixel coordinates of adjacent corner points are used; the actual physical distance between these adjacent corner points is obtained through on-site measurement. Then calculate the conversion factor for this segment. : contrast and If the deviation between the two is within a preset threshold, the average value is taken. This serves as the final scale conversion factor. If the deviation exceeds the limit, re-check the scale placement and image acquisition process, eliminate errors, and recalculate. Traverse all corner point pixel coordinates Use the verified Calculate the actual distance between each adjacent point and fit the boundary of the warning zone; generate pixel polygons of the warning zone in the image using polygon fitting method, convert them into recognizable digital boundary parameters, and complete the calibration.

5. The method for warning of high-risk areas in a structural fatigue loading laboratory according to claim 1, characterized in that, The specific method for obtaining the operating status of the monitored device in S3 is as follows: If the control system of the monitored equipment provides an open interface, the equipment's operating status signal is read directly; if there is no corresponding interface, feature patterns are affixed to the moving parts of the equipment where there is a risk, and video analysis methods are used to determine whether the equipment is in motion.

6. The method for warning of high-risk areas in a structural fatigue loading laboratory according to claim 1, characterized in that, When entering the test area, test personnel in S5 must wear safety helmets, reflective vests, or uniforms as required.

7. A high-risk area warning system for a structural fatigue loading laboratory, characterized in that, include: A camera configuration unit is used to configure the same number of cameras according to the number of sides of the main scale of the experimental equipment; The warning zone setting unit is used to set up a warning zone around the moving parts of the equipment where there is a danger; at the same time, it completes the digital calibration of the warning zone. The monitoring areas of each camera are digitally stored; the monitoring areas of each camera together constitute a comprehensive monitoring of the equipment and the guarded area without blind spots. The monitoring status activation unit is used to acquire the operating status of the monitored device; monitoring is only activated when the monitored device is in operation. The spatiotemporal consistency verification unit is used to verify the temporal consistency of the warning data by comparing the timestamps of "personnel entering the warning zone" captured by different cameras; and to map the pixel coordinates of each camera to a unified physical coordinate system by coordinate transformation, and to compare the position of personnel in the images of different cameras to complete the spatial consistency verification of the warning data; only the data that passes the spatiotemporal consistency verification is used as valid input for risk assessment. The judgment and alarm unit is used to determine whether there are test personnel active in the warning area near the monitored equipment, and to report back to the control center and send an alarm signal to the alarm system. The time consistency verification in the spatiotemporal consistency verification unit specifically includes: set up The timestamp of each camera capturing "personnel entering the restricted area" is... Then the standard deviation of the timestamp set for: in, The average of timestamps; Maximum deviation rate for: The initial base threshold is set based on the camera frame rate and network latency limit. ;like If so, the threshold will be lowered according to the set standard; if The threshold is adjusted upwards according to the set standard, ultimately forming a dynamic threshold. If a single timestamp exists satisfy If outliers are removed, the calculation is recalculated; at the same time, a time consistency confidence level is defined. : When satisfied If the time is consistent, then the time consistency is determined to be consistent; At the same time, the confidence level of time consistency needs to be further examined. ,when When, it is judged as "high confidence consistency"; when When, it is judged as "low confidence consistency"; when If so, it is judged as "inconsistent"; The spatial consistency verification in the spatiotemporal consistency verification unit specifically includes: set up The set of physical installation location coordinates of the cameras is For each camera, establish a mapping relationship from pixel coordinates to physical coordinates using camera intrinsic and extrinsic parameters: If the pixel coordinates of the "person enters the restricted area" event in a certain camera are Then the corresponding physical space coordinates satisfy: Based on the mapping relationship, calculate The physical space coordinates of the same event corresponding to each camera are: ; Calculate the coordinates and spatial mean points 3D Euclidean distance: Define spatial discreteness : The basic spatial threshold is set based on the overlap of the camera's field of view and the calibration accuracy. ;like If so, the threshold will be lowered according to the set standard; if The threshold setting standard has been adjusted upwards, forming a dynamic threshold. ; Calculate the maximum spatial deviation : If a certain coordinate satisfy If so, remove it and recalculate; Compute spatial consistency confidence : When satisfied And the minimum bounding sphere radius of the set of spatial coordinate points If the spatial consistency requirement is met, then the determination is made based on the confidence level of spatial consistency: when When, it is "high confidence spatial consistency"; when When, it is "low confidence consistency"; when If so, it is determined as "spatial inconsistency".

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-6: a method for warning of high-risk areas in a structural fatigue loading laboratory.

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