Monitoring method and system for building engineering construction

Abstract

The invention discloses a monitoring method and system for building engineering construction, and relates to the technical field of construction safety monitoring, and the method comprises the steps: obtaining an environment light field image of a target region and the time sequence positioning data of each moving target, predicting the moving track of each moving target, and obtaining a target region based on the environment light field image and the time sequence positioning data; identifying a static first light source target and a moving second light source target, and identifying a static glare area generated by the first light source target and a dynamic glare area possibly generated by the second light source in a future time period according to the ambient light field image, identifying a moving target which is about to enter a static glare area or a dynamic glare area which is not generated by itself as a risk target by combining the moving track, and generating a light field regulation and control instruction according to the risk target and the static glare area or the dynamic glare area which the risk target is about to enter; the method has the advantages that the static glare area is recognized in real time, the dynamic glare area is predicted, and the light environment is actively regulated and controlled to eliminate visual potential safety hazards.

Description

Technical Field

[0001] This invention relates to the field of construction safety monitoring technology, and in particular to a monitoring method and system for building construction. Background Technology

[0002] Implementing comprehensive and effective monitoring during the construction process is a key step in ensuring construction safety and improving project quality and efficiency.

[0003] Current monitoring methods in construction mainly focus on monitoring the behavior of construction workers or the status of equipment, and generally lack analysis of the nighttime lighting conditions at construction sites. During nighttime construction of large projects, high-power lighting towers set up to supplement lighting and the vehicle lights on mobile equipment will create a multi-source coexistence environment, producing strong light and shadow interlacing, flickering effects and adaptive glare, which in turn creates a large amount of dynamically changing disabling glare. This poor lighting environment will seriously weaken the visual perception and judgment ability of workers, making it easy for personnel and machinery to collide due to poor observation during movement, posing a major safety hazard.

[0004] Therefore, a monitoring method and system for building construction is proposed. Summary of the Invention

[0005] In view of the above-mentioned prior art, this application is hereby filed. Embodiments of this application provide a monitoring method and system for building construction, which can eliminate visual interference and reduce the risk of construction accidents in real time.

[0006] According to one aspect of this application, a monitoring method for construction engineering is provided, comprising: acquiring an ambient light field image within a target area and temporal positioning data of various moving targets; predicting the movement trajectory of each moving target using a trajectory prediction model based on the temporal positioning data; identifying and distinguishing a stationary first light source target and a moving second light source target within the target area based on the ambient light field image and the temporal positioning data; identifying a static glare area generated by the first light source target based on the ambient light field image; predicting a dynamic glare area that may be generated within the target area in the future time period using a ray tracing model based on the movement trajectory corresponding to the second light source target; performing spatiotemporal overlay analysis of the movement trajectory of each moving target with the static glare area and the dynamic glare area to identify moving targets that are about to enter the static glare area or a dynamic glare area not generated by themselves, as risk targets; generating a light field control command based on the risk targets and the static glare area or dynamic glare area they will enter, the light field control command being used to control at least one controllable lighting device to adjust its lighting parameters to eliminate the glare that the risk targets will face before entering the static glare area or dynamic glare area.

[0007] According to another aspect of this application, a monitoring system for construction engineering is provided, comprising: an acquisition module for acquiring an ambient light field image within a target area and temporal positioning data of each moving target; a trajectory prediction module for predicting the movement trajectory of each moving target based on the temporal positioning data using a trajectory prediction model; a classification module for identifying and distinguishing a stationary first light source target and a moving second light source target within the target area based on the ambient light field image and the temporal positioning data; a static glare recognition module for identifying the static glare area generated by the first light source target based on the ambient light field image; and a dynamic glare prediction module for identifying the dynamic glare area based on the second light source target. The system comprises a movement trajectory module, which uses a ray tracing model to predict dynamic glare areas that may occur within the target area in the future; a risk target localization module, which performs spatiotemporal overlay analysis on the movement trajectories of each moving target with the static glare area and the dynamic glare area to identify moving targets that are about to enter the static glare area or the dynamic glare area that they do not generate themselves, as risk targets; and an instruction generation module, which generates a light field control instruction based on the risk target and the static or dynamic glare area it will enter. The light field control instruction is used to control at least one controllable lighting device to adjust its lighting parameters to eliminate the glare that the risk target will face before entering the static or dynamic glare area.

[0008] According to another aspect of this application, an electronic device is provided, including a memory and a processor, the memory being used to store computer-executable instructions, and the processor being used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method described above.

[0009] According to another aspect of this application, a computer storage medium is provided that stores computer-executable instructions thereon, which, when executed by a processor, implement the steps of the method described above.

[0010] Compared with the prior art, the monitoring method and system for construction engineering according to the embodiments of this application can dynamically identify and control glare areas by integrating ambient light field analysis and moving target trajectory prediction, and actively adjust lighting parameters before risky targets enter dangerous areas. It has the advantages of eliminating visual interference in real time and reducing the risk of construction accidents. Attached Figure Description

[0011] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0012] Figure 1 This is a flowchart of a monitoring method for construction engineering projects according to the present invention.

[0013] Figure 2 This is a block diagram of a monitoring system for building construction according to the present invention.

[0014] Figure 3 This is a block diagram of an electronic device according to the present invention. Detailed Implementation

[0015] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0016] Exemplary methods

[0017] Figure 1 The illustration depicts a monitoring method for construction engineering according to an embodiment of this application, comprising: acquiring an ambient light field image within a target area and temporal positioning data of each moving target; predicting the movement trajectory of each moving target using a trajectory prediction model based on the temporal positioning data; identifying and distinguishing a stationary first light source target and a moving second light source target within the target area based on the ambient light field image and the temporal positioning data; identifying a static glare area generated by the first light source target based on the ambient light field image; predicting a dynamic glare area that may be generated within the target area in the future time period using a ray tracing model based on the movement trajectory corresponding to the second light source target; performing spatiotemporal overlay analysis of the movement trajectory of each moving target with the static glare area and the dynamic glare area to identify moving targets that are about to enter the static glare area or a dynamic glare area not generated by themselves, as risk targets; generating a light field control command based on the risk target and the static glare area or dynamic glare area it will enter, the light field control command being used to control at least one controllable lighting device to adjust its lighting parameters to eliminate the glare that the risk target will face before entering the static glare area or dynamic glare area.

[0018] Among them, ambient light field images refer to image data containing light source distribution, brightness and spectral information in the construction site, which are collected by multispectral imaging equipment. Specifically, it can be achieved by using a high dynamic range camera combined with multi-angle supplementary lighting equipment to capture the spatial position and light intensity characteristics of different light sources.

[0019] Among them, time-series positioning data refers to the periodic collection of coordinates and timestamp information of moving targets in three-dimensional space through positioning sensors. Specifically, it can be implemented using UWB ultra-wideband positioning systems or visual SLAM algorithms to track the real-time movement status of construction personnel and mechanical equipment.

[0020] The trajectory prediction model refers to an algorithm model that predicts the future path of a moving target based on historical motion data. Specifically, it can be implemented using Kalman filtering, LSTM neural network or particle filtering algorithms to infer the motion trend of moving targets within the construction area.

[0021] Among them, the ray tracing model refers to the calculation model that simulates the propagation path and reflection and refraction effects of dynamic light sources. Specifically, it can be implemented using Monte Carlo ray tracing or reverse ray projection algorithms to predict the dynamic bright areas formed by moving light sources on the ground or equipment surface in the construction area.

[0022] Spatiotemporal overlay analysis refers to the computational process of jointly matching the trajectory of a moving target with the glare area in the time and space dimensions. Specifically, it can be implemented using a geometric intersection algorithm of a four-dimensional spatiotemporal coordinate system to determine whether a moving target will enter a dangerous glare area at a specific time.

[0023] Among them, the light field control command refers to the operation command to control the intelligent lighting equipment to adjust the beam direction, intensity or color temperature. Specifically, it can be implemented using the DALI digital lighting interface protocol or the DMX512 lighting control protocol to dynamically change the lighting conditions of local areas to eliminate the risk of glare.

[0024] The core innovation of this application lies in establishing a joint prediction mechanism for static and dynamic glare areas by integrating ambient light field analysis and moving target trajectory prediction technologies, and triggering active light field regulation based on spatiotemporal overlay analysis to achieve real-time monitoring and dynamic elimination of glare risks in construction areas.

[0025] In some of the solutions described above in this application, identifying and distinguishing between a stationary first light source target and a moving second light source target within a target area specifically includes the following steps:

[0026] First, image recognition is performed on the ambient light field image to locate all potential light sources and their corresponding physical locations in the ambient light field image.

[0027] Here, potential light sources can be located through image recognition using a semantic segmentation algorithm based on deep learning. Pixel regions with brightness exceeding a set threshold are marked in the ambient light field image as potential light sources. The specific steps for locating their physical positions include:

[0028] The first step is to extract the pixel coordinates of the potential light source in the ambient light field image through image recognition. Here, an edge detection algorithm can be used to extract the geometric center point of the potential light source as the pixel coordinates.

[0029] The second step is to obtain the pre-calibrated camera parameters, including focal length, distortion coefficient, and the camera's installation height and pitch angle in the world coordinate system.

[0030] The third step is to use perspective projection transformation to convert the pixel coordinates to the world coordinate system based on the camera parameters, thereby obtaining the physical position.

[0031] Then, all physical locations are matched with the current location data in all time-series positioning data. If the match is successful, the potential light source corresponding to the physical location is identified as the second light source target. If the match is unsuccessful, the potential light source corresponding to the physical location is identified as the first light source target.

[0032] The matching process here uses the Euclidean distance threshold method. When the distance between the physical location of the light source and the current positioning coordinates of the moving target is less than 0.5 meters, the match is considered successful.

[0033] Through the above technical solution, this application can accurately distinguish between stationary and moving light source targets, providing a foundation for subsequent glare area identification and prediction. This distinction method combines image recognition and localization data matching, improving the accuracy and reliability of identification. Furthermore, this method can adapt to complex construction environments, effectively addressing dynamic changes in the number and location of light sources, providing crucial support for lighting environment management at construction sites.

[0034] In some of the above-mentioned solutions of this application, identifying the static glare region generated by the first light source target includes: performing regional brightness analysis on the ambient light field image to identify a continuous image region whose brightness exceeds a preset glare threshold and has an optical correlation with the first light source target, as the static glare region.

[0035] Among them, the regional brightness analysis calculates the gray value of each pixel in the image and marks areas with more than 5,000 lumens as candidate glare areas.

[0036] Among them, the determination of continuous image regions is achieved by identifying the boundary continuity of candidate glare areas through edge detection algorithms, thereby eliminating isolated bright noise points.

[0037] Specifically, the acquired ambient light field image is first converted to the HSV color space and the luminance component is extracted to generate a two-dimensional luminance distribution map. Then, a glare threshold is set based on human eye comfort experimental data. Next, a threshold segmentation algorithm is used to initially extract all candidate image regions with luminance exceeding the limit. The candidate image regions are then correlated with the identified first light source target. The centroid coordinates of each candidate region are calculated, and the Euclidean distance between the candidate region and the first light source target is calculated. If the distance is less than a preset threshold, the candidate region is considered to have an optical correlation with the first light source target. Finally, the candidate image regions that meet the luminance threshold, area threshold, and optical correlation with the first light source target are identified as static glare regions. These regions are marked in the image and their spatial range information is recorded for subsequent risk analysis.

[0038] Through the above technical solution, this application can accurately identify the static glare area generated by a stationary light source, providing a reliable data basis for subsequent risk assessment and early warning.

[0039] In some of the above-mentioned schemes of this application, the spatiotemporal overlay analysis includes: calculating the intersection of the predicted trajectory of the moving target with each static glare area and a dynamic glare area not generated by the target itself; if the moving trajectory intersects with a static glare area, the moving target is identified as a risk target; if the moving trajectory intersects with a dynamic glare area generated by a non-moving target, then: extract the velocity vector of the moving target at the current moment based on the time-series positioning data; calculate the estimated time for the moving target to reach the dynamic glare area based on the velocity vector and the position of the intersection point; compare the estimated time with the generation time interval corresponding to the dynamic glare area; if the estimated time is within the generation time interval corresponding to the dynamic glare area, the moving target is identified as a risk target.

[0040] The intersection calculation uses a geometric algorithm to determine the spatial overlap between the moving trajectory and the glare area.

[0041] Specifically, when the predicted trajectory of a moving target intersects with the dynamic glare area, the instantaneous velocity direction and magnitude of the moving target at the current moment are extracted and combined with the spatial location of the intersection point to calculate the estimated time for the moving target to reach the intersection point. If the time interval of the dynamic glare area covers the estimated time, it is determined that the moving target will enter the area during the period when the dynamic glare area is in effect, thereby triggering a risk warning.

[0042] For example, if a dynamic glare area exists within the time interval [10:00:00, 10:00:30], and the estimated time for the moving target to reach the intersection is 10:00:15, then the moving target is identified as a risk target. This process, by introducing a time-dimensional matching mechanism, avoids misjudgments caused solely by spatial overlap, thus improving the accuracy of risk target identification.

[0043] Through the above technical solution, this application can accurately identify moving targets that are about to enter the glare area, thus improving the early warning capability for potential risks.

[0044] In some of the above-mentioned schemes of this application, extracting the velocity vector of the moving target at the current moment based on the temporal positioning data specifically includes: obtaining the position of the moving target at the previous sampling moment and the position at the current sampling moment based on the temporal positioning data of the moving target; calculating the Euclidean distance between the position at the previous sampling moment and the position at the current sampling moment; calculating the magnitude of the instantaneous velocity of the moving target at the current moment based on the Euclidean distance and the timestamp difference between the previous sampling moment and the current sampling moment; and taking the vector direction from the position at the previous sampling moment to the position at the current sampling moment as the direction of the instantaneous velocity to obtain the velocity vector at the current moment.

[0045] When obtaining the position of a moving target at two adjacent sampling times, it is necessary to ensure that the timestamp difference is small enough to reduce the impact of sudden changes in motion state.

[0046] The Euclidean distance is calculated by taking the square root of the sum of the squares of the differences in three-dimensional spatial coordinates, which is suitable for non-linear trajectory scenarios.

[0047] The magnitude of the instantaneous velocity is obtained by dividing the Euclidean distance by the timestamp difference, and the direction is directly determined by the position change vector, thus avoiding the introduction of angle calculation errors.

[0048] The above technical solution utilizes existing positioning data for speed calculation, avoiding the deployment of additional speed sensors and reducing system complexity and cost.

[0049] In some of the schemes described above in this application, the generation of light field manipulation commands includes:

[0050] If the target is about to enter a static glare area, an instruction is generated to control the controllable lighting equipment to emit a compensation beam to the static glare area in order to increase the base illuminance of the area.

[0051] The root cause of static glare is usually the improper position, angle, or light distribution design of high-power lighting equipment. Its harm is not mainly due to the light source itself directly shining into the eyes, but rather the strong shadow blind spots it creates on the backlit side.

[0052] For example, a bright lighthouse illuminates area A, but its light cannot reach the area behind the equipment next to area A, creating a dark shadow area with extremely high contrast. When workers walk from the bright area to this shadow area, their eyes cannot adapt and they feel as if they are "blind," making them very likely to trip or collide with obstacles in the shadow area.

[0053] In summary, the problem with static glare areas is essentially insufficient local illumination. Therefore, the most direct and effective solution is to increase the overall brightness of the area and eliminate the dangerous contrast between light and dark. Thus, when a risky target is about to enter a static glare area, illumination compensation is required for the static glare area.

[0054] If the target is about to enter a dynamic glare area, an instruction is generated to control the controllable lighting device to emit a suppressor beam onto the path of the dynamic glare area to neutralize or reduce the glare generated by the second light source target.

[0055] The root cause of dynamic glare is strong light sources moving in the air (such as vehicle headlights) that directly or indirectly enter the human eye through a reflective surface. The harm is that the instantaneous strong light overload causes the pupils to contract sharply, temporarily lose visual adaptation ability, and make it impossible to see the environment in front.

[0056] For example, when an excavator is turning, its headlights suddenly sweep across a worker's line of sight, or the headlights shine on a smooth metal surface, and the reflected light happens to dazzle the eyes of the crane operator.

[0057] In summary, the dynamic glare area itself is already too bright, or even exceeds the light intensity limit. Therefore, when a risky target is about to enter the dynamic glare area, it is necessary to emit a beam of "negative light" with a specific spectrum, angle and intensity (for example, light with the opposite phase to the harmful glare) into the propagation path of the glare beam, so as to physically achieve the interference cancellation of light waves and directly weaken the glare intensity from the path.

[0058] Exemplary System

[0059] Figure 2The illustration shows a monitoring system for construction engineering according to an embodiment of this application, including: an acquisition module for acquiring an ambient light field image within a target area and temporal positioning data of each moving target; a trajectory prediction module for predicting the movement trajectory of each moving target based on the temporal positioning data using a trajectory prediction model; a classification module for identifying and distinguishing a stationary first light source target and a moving second light source target within the target area based on the ambient light field image and the temporal positioning data; a static glare recognition module for identifying the static glare area generated by the first light source target based on the ambient light field image; and a dynamic glare prediction module for identifying the moving glare area corresponding to the second light source target. The system consists of a motion trajectory module, which uses a ray tracing model to predict potential dynamic glare areas within the target area over a future time period; a risk target localization module, which performs spatiotemporal overlay analysis of the movement trajectories of each moving target with static and dynamic glare areas to identify moving targets about to enter static glare areas or dynamic glare areas not generated by themselves, as risk targets; and an instruction generation module, which generates light field control instructions based on the risk targets and the static or dynamic glare areas they will enter. These instructions control at least one controllable lighting device to adjust its lighting parameters to eliminate the glare the risk targets will face before entering the static or dynamic glare areas.

[0060] In one example, identifying and distinguishing between a stationary first light source target and a moving second light source target within a target area includes: performing image recognition on an ambient light field image to locate all potential light sources and their corresponding physical locations in the ambient light field image; matching all physical locations with the current location data in all temporal location data; if a match is successful, the potential light source corresponding to the physical location is identified as the second light source target; if a match is unsuccessful, the potential light source corresponding to the physical location is identified as the first light source target.

[0061] In one example, locating the physical location of a potential light source includes: extracting the pixel coordinates of the potential light source in the ambient light field image through image recognition; obtaining pre-calibrated camera parameters; and transforming the pixel coordinates to the world coordinate system based on the camera parameters to obtain the physical location.

[0062] In one example, identifying the static glare region generated by the first light source target includes: performing regional brightness analysis on the ambient light field image to identify continuous image regions whose brightness exceeds a preset glare threshold and which are optically associated with the first light source target, as static glare regions.

[0063] In one example, the spatiotemporal overlay analysis includes: calculating the intersection of the predicted trajectory of the moving target with each static glare region and a dynamic glare region not generated by the target itself; if the trajectory intersects with a static glare region, the moving target is identified as a risk target; if the trajectory intersects with a dynamic glare region not generated by the moving target, then: extract the velocity vector of the moving target at the current moment based on the temporal positioning data; calculate the estimated time for the moving target to reach the dynamic glare region based on the velocity vector and the position of the intersection point; compare the estimated time with the generation time interval corresponding to the dynamic glare region; if the estimated time is within the generation time interval corresponding to the dynamic glare region, the moving target is identified as a risk target.

[0064] In one example, extracting the velocity vector of a moving target at the current moment from temporal positioning data includes: obtaining the position of the moving target at the previous sampling moment and the position at the current sampling moment based on the temporal positioning data of the moving target; calculating the Euclidean distance between the positions at the previous sampling moment and the current sampling moment; calculating the magnitude of the instantaneous velocity of the moving target at the current moment based on the Euclidean distance and the timestamp difference between the previous sampling moment and the current sampling moment; and taking the vector direction from the position at the previous sampling moment to the position at the current sampling moment as the direction of the instantaneous velocity to obtain the velocity vector at the current moment.

[0065] In one example, the generated light field modulation instructions include: if the risk target is about to enter a static glare area, generating an instruction to control the controllable lighting device to emit a compensation beam to the static glare area to increase the base illuminance of the area; if the risk target is about to enter a dynamic glare area, generating an instruction to control the controllable lighting device to emit a suppression beam to the generation path of the dynamic glare area to neutralize or weaken the glare generated by the second light source target.

[0066] Exemplary electronic devices

[0067] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0068] like Figure 3 As shown, the electronic device includes one or more processors and memory.

[0069] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0070] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the driving behavior decision-making methods of the various embodiments of this application described above, and / or other desired functions.

[0071] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0072] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device may include any other suitable components depending on the specific application.

[0073] Exemplary computer-readable media

[0074] Embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the driving behavior decision-making methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0075] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0076] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0077] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0078] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0079] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0080] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A monitoring method for construction work, characterized by, The method comprises: acquiring an ambient light field image in a target area and time-series positioning data of each moving target; predicting a moving track of each moving target by a track prediction model based on the time-series positioning data; identifying and distinguishing a first light source target that is static and a second light source target that is moving in the target area based on the ambient light field image and the time-series positioning data; identifying a static glare area generated by the first light source target according to the ambient light field image; predicting a dynamic glare area that is likely to be generated in the target area in a future time period by a ray tracing model based on a moving track corresponding to the second light source target; performing spatio-temporal superposition analysis on the moving track of each moving target and the static glare area and the dynamic glare area, identifying a moving target that is about to enter a static glare area or a dynamic glare area not generated by itself as a risk target; generating a light field regulation instruction according to the risk target and the static glare area or the dynamic glare area that the risk target is about to enter, the light field regulation instruction being used to control at least one controllable lighting device to adjust its lighting parameters so as to eliminate the glare that the risk target will face before the risk target enters the static glare area or the dynamic glare area.

2. A monitoring method for construction work according to claim 1, characterized in that, The identifying and distinguishing of the first light source target that is static and the second light source target that is moving in the target area comprises: performing image recognition on the ambient light field image to locate all potential light sources in the ambient light field image and corresponding physical positions; matching all the physical positions with the positioning data at the current time in all the time-series positioning data, if the matching is successful, identifying the potential light source corresponding to the physical position as the second light source target, and if the matching is not successful, identifying the potential light source corresponding to the physical position as the first light source target.

3. A monitoring method for construction work according to claim 2, characterized in that, The locating of the physical position corresponding to the potential light source comprises: extracting pixel coordinates of the potential light source in the ambient light field image by image recognition; acquiring pre-calibrated camera parameters; converting the pixel coordinates to a world coordinate system according to the camera parameters to obtain the physical position.

4. The monitoring method for construction work according to claim 1, wherein The identifying of the static glare area generated by the first light source target comprises: performing region brightness analysis on the ambient light field image to identify a continuous image region whose brightness exceeds a preset glare threshold and that has optical correlation with the first light source target as the static glare area.

5. The monitoring method for construction work according to claim 1, wherein The spatio-temporal superposition analysis comprises: performing intersection calculation on the predicted track of the moving target and each of the static glare area and the dynamic glare area not generated by the moving target, if the moving track has an intersection point with the static glare area, the moving target is determined as a risk target, and if the moving track has an intersection point with the dynamic glare area not generated by the moving target, then: extracting a velocity vector of the moving target at the current time according to the time-series positioning data; calculating a predicted time for the moving target to reach the dynamic glare area according to the velocity vector and the position of the intersection point. The predicted time is compared with a generation time interval corresponding to the dynamic glare region, and if the predicted time is within the generation time interval corresponding to the dynamic glare region, the moving target is determined as the risk target.

6. A monitoring method for construction work according to claim 5, wherein The speed vector of the moving target at the current time is extracted according to the time sequence positioning data, including: According to the time sequence positioning data of the moving target, the position of the moving target at the last sampling time and the position of the moving target at the current sampling time are obtained; The Euclidean distance between the position at the last sampling time and the position at the current sampling time is calculated; According to the Euclidean distance and the timestamp difference between the last sampling time and the current sampling time, the magnitude of the instantaneous speed of the moving target at the current time is calculated; The direction of the vector from the position at the last sampling time to the position at the current sampling time is taken as the direction of the instantaneous speed, and the speed vector at the current time is obtained.

7. The monitoring method for construction work according to claim 1, wherein The generation of the light field regulation instruction includes: If the risk target is about to enter a static glare region, an instruction is generated to control the controllable lighting device to emit a compensation light beam to the static glare region to improve the basic illumination of the region; If the risk target is about to enter a dynamic glare region, an instruction is generated to control the controllable lighting device to emit an inhibition light beam to the generation path of the dynamic glare region to neutralize or weaken the glare generated by the second light source target.

8. A monitoring system for construction engineering works, characterized in that It includes: An acquisition module is configured to acquire an ambient light field image in a target region and time sequence positioning data of each moving target; A trajectory prediction module is configured to predict a moving trajectory of each moving target based on the time sequence positioning data through a trajectory prediction model; A classification module is configured to identify and distinguish a first light source target that is stationary and a second light source target that is moving in the target region based on the ambient light field image and the time sequence positioning data; A static glare identification module is configured to identify a static glare region generated by the first light source target according to the ambient light field image; A dynamic glare prediction module is configured to identify a moving trajectory corresponding to the second light source target and predict a dynamic glare region that may be generated in the target region in a future time period through a ray tracing model; A risk target positioning module is configured to perform spatiotemporal superposition analysis on the moving trajectory of each moving target and the static glare region and the dynamic glare region, and identify a moving target that is about to enter a static glare region or a dynamic glare region not generated by itself as a risk target; An instruction generation module is configured to generate a light field regulation instruction according to the risk target and the static glare region or the dynamic glare region that the risk target is about to enter, and the light field regulation instruction is used to control at least one controllable lighting device to adjust its lighting parameters to eliminate the glare that the risk target will face before entering the static glare region or the dynamic glare region. 9.An electronic device comprising a memory and a processor, the electronic device characterized by: The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the method of any one of claims 1-7.

10. A computer storage medium having stored thereon computer- executable instructions, comprising: The computer executable instructions, when executed by the processor, implement the steps of the method according to any one of claims 1-7.

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