Digital supervision management method and system applied to engineering project

Abstract

The invention relates to the technical field of project supervision, in particular to a digital supervision management method and system applied to an engineering project. The method comprises the following steps: processing a construction site image to obtain a processed construction site image, carrying out category division on the processed construction site image to obtain a historical site image set and a current site image set, carrying out key anomaly analysis on the historical site image set, and obtaining a current site image set; obtaining a key monitoring position and an exception processing image set, screening the current field image set based on the key monitoring position to obtain a first priority image, judging whether the first priority image has image exception, if yes, performing similarity processing on the first priority image according to the exception processing image set, and if not, performing similarity processing on the first priority image; and obtaining the construction information, judging whether preset construction abnormity exists in the construction information or not, if so, generating an alarm instruction, and controlling the alarm equipment to carry out alarm output. According to the invention, the supervision efficiency of the construction site is improved.

Description

Technical Field

[0001] The present application relates to the technical field of project supervision, and in particular to a digital supervision management method and system applied to engineering projects. Background Art

[0002] When implementing power grid projects, it is usually necessary to supervise their safety and quality. At present, the safety and quality supervision system of my country's engineering construction industry can be divided from different perspectives. For example, according to the management level, it can be divided into government safety and quality supervision, enterprise safety and quality management, and project safety and quality control.

[0003] At present, the supervision and management of power grids, which is based on the life cycle of project implementation, can be divided into several key stages, including: safety education and training supervision, construction site supervision, material quality supervision, quality acceptance and evaluation supervision, and construction record and file management supervision. These supervision measures together constitute the safety and quality supervision system of power grid projects during the construction phase, providing a strong guarantee for the smooth implementation and operation of power grid projects.

[0004] Among the key steps of power grid supervision and management, construction site supervision is one of the links with the longest implementation cycle and the greatest difficulty in supervision. Common methods for construction site supervision are to obtain the scene picture of the construction site through cameras, and then perform image detection on the scene picture to determine whether there are abnormal construction behaviors and abnormal operating facilities in the scene picture. If there are, the electronic equipment generates an alarm command to control the alarm device to alarm, and inform the staff to clear the abnormality in time.

[0005] Regarding the common technologies mentioned above, the inventors believe that due to the influence of various factors, such as occlusion, viewing angle, etc., when construction supervision is carried out through image detection technology, abnormal construction image targets will be mistakenly identified as normal construction image targets, or normal construction image targets will be mistakenly identified as abnormal construction image targets, resulting in underreporting or false reporting, thereby reducing the efficiency of supervision on the construction site. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present application provides a digital supervision management method and system applied to engineering projects.

[0007] In the first aspect, the present application provides a digital supervision management method applied to engineering projects, which adopts the following technical solutions:

[0008] A digital supervision management method applied to engineering projects, comprising:

[0009] Acquire construction site images, where the construction site images are site images corresponding to different time nodes in a preset construction site area;

[0010] Performing fine processing on the construction site image to obtain a processed construction site image;

[0011] Classify the processed construction site images according to time nodes to obtain a historical site image set and a current site image set;

[0012] Performing key anomaly analysis on the historical on-site image set to obtain key monitoring locations of the construction site and an anomaly processing image set corresponding to the key monitoring locations;

[0013] Filtering the current scene image set based on the key monitoring position to obtain a first priority image where the key monitoring position exists;

[0014] Determine whether the first priority image has an image anomaly, and if so, perform similarity processing on the first priority image according to the abnormal processing image set to obtain construction information of the first priority image;

[0015] It is determined whether there is a preset construction anomaly in the construction information. If so, an alarm instruction is generated to control the alarm device to output an alarm.

[0016] By adopting the above technical solution, the construction site images are obtained, and the on-site conditions at different time nodes in the preset construction site area are covered. Comprehensive monitoring of the construction site is ensured, providing a rich data basis for subsequent analysis. After fine processing of the image, the details of the site can be more clearly identified, providing high-quality image data for subsequent analysis. The processed construction site images are classified by time nodes to obtain a historical site image set and a current site image set, thereby realizing effective management of the construction site images. By performing key abnormality analysis on the historical images, the key monitoring positions and abnormal processing image sets of the construction site can be accurately located, providing strong support for subsequent rapid response to abnormalities. The current image is filtered based on the key monitoring position to obtain the first priority image, thereby improving the efficiency of abnormal monitoring. By performing abnormality judgment on the first priority image, it is determined whether there are abnormalities such as occlusion that affect image monitoring. If there are, the first priority image is processed for similarity based on the abnormal processing image set, and the construction information corresponding to the occlusion abnormality in the first priority image is quickly obtained, and it is accurately judged whether there are preset abnormalities in the construction. Once an abnormality is detected, an alarm command is immediately generated to control the output of the alarm equipment, realizing real-time monitoring and timely early warning of the construction site, improving the effective and safe management level of the construction site, and thereby improving the efficiency of supervision of the construction site.

[0017] In a preferred example, the present application may be further configured as follows: the fine processing of the construction site image to obtain the processed construction site image includes:

[0018] Performing edge filtering on the construction site image to obtain standard contour layers of different construction workers and construction equipment at the construction site and key contour layers of behavior details of the construction workers and operating status details of the construction equipment at the construction site;

[0019] Divide the standard contour layer into a plurality of sub-regions, and collect a regional histogram corresponding to each sub-region and an overall histogram corresponding to the standard contour layer, as well as a regional flatness corresponding to each sub-region and an overall flatness corresponding to the standard contour layer;

[0020] Determine an overall contrast curve and an overall brightness curve corresponding to the standard contour layer according to the overall histogram and the overall flatness;

[0021] Determine a regional contrast curve corresponding to each sub-region according to the sub-region histogram, and fuse the regional contrast curve with the overall contrast curve to obtain a fused contrast curve;

[0022] Determining a brightness curve adjustment weight corresponding to each sub-region according to the flatness of the sub-region, and fusing the overall brightness curve with the fused contrast curve based on the brightness curve adjustment weight to obtain a parameter adjustment curve;

[0023] Adjusting each sub-region of the standard contour layer according to the parameter adjustment curve to obtain an adjusted standard contour layer;

[0024] The processed construction site image is obtained by fusing the adjusted standard contour layer with the key contour layer.

[0025] In a preferred example, the present application may be further configured as follows: performing key abnormality analysis on the historical on-site image set to obtain key monitoring locations of the construction site and a set of abnormality processing images corresponding to the key monitoring locations, including:

[0026] Performing an abnormality analysis on each image in the historical scene image set to obtain a shooting area and an image abnormality type corresponding to each image;

[0027] Summarize the abnormal marking points according to the shooting areas to obtain a first marking point set corresponding to each shooting area at different time nodes;

[0028] Statistically dividing the abnormal mark point set according to the same image position coordinates to obtain a second mark point set corresponding to different position coordinates of each shooting area;

[0029] Determine whether the number of abnormal marking points corresponding to the second marking point set exceeds a preset abnormal number. If so, mark the position of the shooting area corresponding to the number of abnormal marking points to obtain a key monitoring position of the construction site;

[0030] The exception handling information corresponding to the key monitoring position is collected, and the exception handling information, the shooting area and the image exception type are respectively bound to the corresponding historical scene images to obtain the exception handling image set corresponding to the key monitoring position.

[0031] In a preferred example, the present application may be further configured as follows: performing similarity processing on the first priority image according to the abnormal processing image set to obtain construction information of the first priority image includes:

[0032] Determine, based on the current scene image set, a common view scene image that is adjacent to the time position node of the first priority image and has the key monitoring position;

[0033] Performing three-dimensional simulation processing based on the first priority image and the common viewing scene image to obtain an abnormal three-dimensional image having the image abnormality;

[0034] Determine abnormal coordinate information in the abnormal three-dimensional image and abnormal descriptor information corresponding to the abnormal coordinate information;

[0035] Performing a multi-dimensional spatial data search on the abnormal images in the abnormal processing image set based on the image feature points of the key monitoring positions in the abnormal three-dimensional images to obtain a matching image set;

[0036] Performing a similarity analysis between the first priority image and each abnormal image in the matching image set according to the abnormal coordinate information and the abnormal description sub-information to obtain similarity information;

[0037] Based on the similarity information, a similarity mean is determined, and it is judged whether the similarity mean satisfies a preset similarity range. If so, the target processing information, target area and target exception type corresponding to the similarity mean in the exception processing image set are determined, and the target processing information, the target area and the target exception type are respectively bound to the first priority image to obtain the construction information of the first priority image.

[0038] In a preferred example, the present application may be further configured as follows: the step of determining whether the first priority image has an image abnormality may further include:

[0039] If there is no image anomaly in the first priority image, determining based on the current on-site image set that there is no second priority image of the key monitoring location;

[0040] Determine whether there is an image abnormality in the second priority image. If there is an image abnormality, determine a monitoring abnormality position where the image abnormality exists in the second priority image, generate a UAV flight instruction according to the monitoring abnormality position, and control the UAV to fly from an initial position to the monitoring abnormality position for monitoring;

[0041] Collect the construction monitoring image sent by the drone, and determine whether the construction monitoring image has a preset construction anomaly, and if so, determine the to-be-processed information and the to-be-processed type of the construction monitoring image based on the construction anomaly standard;

[0042] The key monitoring positions of the construction site and the abnormality processing image set corresponding to the key monitoring positions are pre-updated according to the monitored abnormality positions, the information to be processed and the type to be processed to obtain updated key monitoring positions and updated abnormality processing image set.

[0043] In a preferred example, the present application may be further configured as follows: pre-updating the key monitoring positions of the construction site and the abnormal processing image set corresponding to the key monitoring positions according to the abnormal monitoring positions, the information to be processed and the type to be processed, to obtain the updated key monitoring positions and the updated abnormal processing image set, and then further comprising:

[0044] Acquire data reflecting that the first priority image has image abnormality within a preset time;

[0045] Determine the abnormal frequency occurring at the key monitoring position according to the reflected data, and determine whether the abnormal frequency exceeds a preset abnormal frequency;

[0046] When the abnormal frequency does not exceed the preset abnormal frequency, the key monitoring position and the abnormal processing image set corresponding to the key monitoring position are eliminated and updated to obtain an updated key monitoring position and abnormal processing image set.

[0047] In a preferred example, the present application may be further configured as follows: performing an abnormality analysis on each image in the historical scene image set to obtain the shooting area and image abnormality type corresponding to each image includes:

[0048] Each image in the historical scene image set is input into a preset anomaly detection model for anomaly detection analysis to obtain the shooting area and image anomaly type corresponding to each image.

[0049] In the second aspect, the present application provides a digital supervision management system applied to engineering projects, which adopts the following technical solutions:

[0050] A digital supervision management system applied to engineering projects, comprising:

[0051] An image acquisition module is used to acquire construction site images, where the construction site images are site images corresponding to different time nodes in a preset construction site area;

[0052] An image processing module is used to perform fine processing on the construction site image to obtain a processed construction site image;

[0053] An image classification module is used to classify the processed construction site images according to time nodes to obtain a historical site image set and a current site image set;

[0054] An anomaly analysis module, used to perform a key anomaly analysis on the historical on-site image set to obtain a key monitoring position of the construction site and an anomaly processing image set corresponding to the key monitoring position;

[0055] An image screening module, configured to screen the current on-site image set based on the key monitoring position to obtain a first priority image that exists at the key monitoring position;

[0056] a similarity processing module, configured to determine whether the first priority image has an image anomaly, and if so, perform similarity processing on the first priority image according to the anomaly processing image set to obtain construction information of the first priority image;

[0057] The alarm control module determines whether there is a preset construction anomaly in the construction information. If so, an alarm instruction is generated to control the alarm device to output an alarm.

[0058] In a possible implementation, when the image processing module performs fine processing on the construction site image to obtain the processed construction site image, it is specifically used to:

[0059] Performing edge filtering on the construction site image to obtain standard contour layers of different construction workers and construction equipment at the construction site and key contour layers of behavior details of the construction workers and operating status details of the construction equipment at the construction site;

[0060] Divide the standard contour layer into a plurality of sub-regions, and collect a regional histogram corresponding to each sub-region and an overall histogram corresponding to the standard contour layer, as well as a regional flatness corresponding to each sub-region and an overall flatness corresponding to the standard contour layer;

[0061] Determine an overall contrast curve and an overall brightness curve corresponding to the standard contour layer according to the overall histogram and the overall flatness;

[0062] Determine a regional contrast curve corresponding to each sub-region according to the sub-region histogram, and fuse the regional contrast curve with the overall contrast curve to obtain a fused contrast curve;

[0063] Determining a brightness curve adjustment weight corresponding to each sub-region according to the flatness of the sub-region, and fusing the overall brightness curve with the fused contrast curve based on the brightness curve adjustment weight to obtain a parameter adjustment curve;

[0064] Adjusting each sub-region of the standard contour layer according to the parameter adjustment curve to obtain an adjusted standard contour layer;

[0065] The processed construction site image is obtained by fusing the adjusted standard contour layer with the key contour layer.

[0066] In another possible implementation, when the anomaly analysis module performs a focused anomaly analysis on the historical site image set to obtain a focused monitoring location of the construction site and an anomaly processing image set corresponding to the focused monitoring location, the anomaly analysis module is specifically used to:

[0067] Performing an abnormality analysis on each image in the historical scene image set to obtain a shooting area and an image abnormality type corresponding to each image;

[0068] Summarize the abnormal marking points according to the shooting areas to obtain a first marking point set corresponding to each shooting area at different time nodes;

[0069] Statistically dividing the abnormal mark point set according to the same image position coordinates to obtain a second mark point set corresponding to different position coordinates of each shooting area;

[0070] Determine whether the number of abnormal marking points corresponding to the second marking point set exceeds a preset abnormal number, and if so, mark the position of the shooting area corresponding to the number of abnormal marking points to obtain a key monitoring position of the construction site;

[0071] The exception handling information corresponding to the key monitoring position is collected, and the exception handling information, the shooting area and the image exception type are respectively bound to the corresponding historical scene images to obtain the exception handling image set corresponding to the key monitoring position.

[0072] In another possible implementation, when the similarity processing module performs similarity processing on the first priority image according to the exception processing image set to obtain the construction information of the first priority image, it is specifically used to:

[0073] Determine, based on the current scene image set, a common view scene image that is adjacent to the time position node of the first priority image and has the key monitoring position;

[0074] Performing three-dimensional simulation processing based on the first priority image and the common viewing scene image to obtain an abnormal three-dimensional image having the image abnormality;

[0075] Determine abnormal coordinate information in the abnormal three-dimensional image and abnormal descriptor information corresponding to the abnormal coordinate information;

[0076] Performing a multi-dimensional spatial data search on the abnormal images in the abnormal processing image set based on the image feature points of the key monitoring positions in the abnormal three-dimensional images to obtain a matching image set;

[0077] Performing a similarity analysis between the first priority image and each abnormal image in the matching image set according to the abnormal coordinate information and the abnormal description sub-information to obtain similarity information;

[0078] Based on the similarity information, a similarity mean is determined, and it is judged whether the similarity mean satisfies a preset similarity range. If so, the target processing information, target area and target exception type corresponding to the similarity mean in the exception processing image set are determined, and the target processing information, the target area and the target exception type are respectively bound to the first priority image to obtain the construction information of the first priority image.

[0079] In another possible implementation, the system further includes: an image determination module, a flight control module, a flight monitoring module, and a data update module, wherein:

[0080] The image determination module is used to determine, based on the current on-site image set, that there is no second priority image at the key monitoring location when there is no image anomaly in the first priority image;

[0081] The flight control module is used to determine whether there is an image abnormality in the second priority image, and if there is an image abnormality, determine a monitoring abnormality position where the image abnormality exists in the second priority image, and generate a UAV flight instruction according to the monitoring abnormality position, and control the UAV to fly from an initial position to the monitoring abnormality position for monitoring;

[0082] The flight monitoring module is used to collect the construction monitoring images sent by the drone, and determine whether there are preset construction anomalies in the construction monitoring images. If so, determine the information to be processed and the type to be processed of the construction monitoring images based on the construction anomaly standard;

[0083] The data update module is used to pre-update the key monitoring positions of the construction site and the exception processing image set corresponding to the key monitoring positions according to the monitored abnormal positions, the information to be processed and the type to be processed, so as to obtain the updated key monitoring positions and the updated exception processing image set.

[0084] In another possible implementation, the system further includes: a data acquisition module, a frequency determination module and a data elimination module, wherein:

[0085] The data acquisition module is used to acquire data reflecting that the first priority image has an image abnormality within a preset time;

[0086] The frequency determination module is used to determine the abnormal frequency occurring at the key monitoring position according to the reflected data, and determine whether the abnormal frequency exceeds a preset abnormal frequency;

[0087] The data elimination module is used to eliminate and update the key monitoring position and the abnormal processing image set corresponding to the key monitoring position when the abnormal frequency does not exceed the preset abnormal frequency, so as to obtain an updated key monitoring position and abnormal processing image set.

[0088] In another possible implementation, when the anomaly analysis module performs an anomaly analysis on each image in the historical scene image set to obtain the shooting area and image anomaly type corresponding to each image, it is specifically used to:

[0089] Each image in the historical scene image set is input into a preset anomaly detection model for anomaly detection analysis to obtain the shooting area and image anomaly type corresponding to each image.

[0090] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0091] at least one processor;

[0092] Memory;

[0093] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned digital supervision management method applied to engineering projects.

[0094] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0095] A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute a digital supervision management method applied to an engineering project.

[0096] In summary, the present application includes at least one of the following beneficial technical effects:

[0097] Acquire construction site images and cover the on-site conditions at different time nodes in the preset construction site area. Ensure comprehensive monitoring of the construction site and provide a rich data foundation for subsequent analysis. After fine processing of the image, the details of the site can be more clearly identified, providing high-quality image data for subsequent analysis. The processed construction site images are classified by time nodes to obtain a historical site image set and a current site image set, thereby achieving effective management of the construction site images. Performing key anomaly analysis on historical images can accurately locate the key monitoring locations of the construction site and the anomaly processing image set, providing strong support for subsequent rapid response to anomalies. Filter the current image based on the key monitoring location to obtain the first priority image, thereby improving the efficiency of anomaly monitoring. By performing anomaly judgment on the first priority image, determine whether there are currently anomalies such as occlusion that affect image monitoring. If so, perform similarity processing on the first priority image based on the anomaly processing image set, quickly obtain the construction information corresponding to the occlusion anomaly in the first priority image, and accurately determine whether there are preset anomalies in the construction. Once an abnormality is detected, an alarm command is immediately generated to control the output of the alarm equipment, realizing real-time monitoring and timely early warning of the construction site, improving the effective and safe management level of the construction site, and thus improving the efficiency of supervision of the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 A flowchart of a digital supervision management method for an engineering project provided in an embodiment of the present application;

[0099] Figure 2 A schematic diagram of the structure of a digital supervision management system applied to an engineering project provided in an embodiment of the present application;

[0100] Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0101] The following is combined with Figure 1 To Attachment Figure 3 This application is described in further detail.

[0102] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0103] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0104] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0105] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0106] The embodiment of the present application provides a digital supervision management method for engineering projects, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and the embodiment of the present application does not limit this. Figure 1 As shown, the method includes:

[0107] Step S10: Acquire construction site images.

[0108] The construction site images are site images corresponding to different time nodes in a preset construction site area.

[0109] For the embodiment of the present application, the construction site image represents the actual picture of the construction site captured by photography or video equipment during the construction process. These images are generally used to record construction progress, check construction quality or monitor site safety and other situations. The preset construction site area refers to a specific building construction area determined and demarcated in advance, and this area is the target range for acquiring images. Different time nodes are used to represent different time periods or time points at the construction site, such as the initial, mid-term, and late stages of construction, or different time periods of each day (such as morning, afternoon, and evening).

[0110] In the embodiment of the present application, the existing monitoring system is used. First, a monitoring camera is installed at the construction site, and it is ensured that the camera's viewing angle can cover the entire preset area; second, the recording parameters of the monitoring system are configured, such as recording time, resolution, and storage location; finally, the monitoring system will automatically record images of the construction site at the set time node and save the image data to a designated storage device.

[0111] Step S11: performing fine processing on the construction site image to obtain a processed construction site image.

[0112] For the embodiment of the present application, the edge filtering process is performed on the construction site image to obtain the standard contour layer of different construction personnel and construction equipment at the construction site and the key contour layer of the behavior details of the construction personnel and the operating status details of the construction equipment at the construction site, the standard contour layer is divided into multiple sub-areas, and the regional histogram corresponding to each sub-area and the overall histogram corresponding to the standard contour layer and the regional flatness corresponding to each sub-area and the overall flatness corresponding to the standard contour layer are collected, and the overall contrast curve and the overall brightness curve corresponding to the standard contour layer are determined according to the overall histogram and the overall flatness, and the regional contrast curve corresponding to each sub-area is determined according to the sub-area histogram, and the regional contrast curve is merged with the overall contrast curve to obtain a fused contrast curve. The brightness curve adjustment weight corresponding to each sub-area is determined according to the sub-area flatness, and based on the brightness curve adjustment weight, the overall brightness curve is merged with the fused contrast curve to obtain a parameter adjustment curve, and each sub-area of ​​the standard contour layer is adjusted according to the parameter adjustment curve to obtain an adjusted standard contour layer, and the adjusted standard contour layer is merged with the key contour layer to obtain a processed construction site image.

[0113] Step S12: Classify the processed construction site images according to time nodes to obtain a historical site image set and a current site image set.

[0114] Specifically, the historical site image set refers to a set of construction site images at one or more time points in the past, which is used to review and analyze historical situations. The current site image set refers to a set of construction site images at the most recent or current time point, which is used for real-time monitoring and current status analysis.

[0115] For the embodiment of the present application, the time information in the image is extracted by image recognition technology (such as OCR). Then, according to the preset time node rules, it is determined whether the image belongs to the historical or current category. The image is stored in the corresponding historical scene image set and current scene image set respectively.

[0116] Step S13: performing key anomaly analysis on the historical on-site image set to obtain key monitoring locations of the construction site and an anomaly processing image set corresponding to the key monitoring locations.

[0117] For the embodiment of the present application, an abnormal analysis is performed on each image in the historical site image set to obtain the shooting area and image abnormality type corresponding to each image. Then, the abnormal marking points are summarized according to the shooting area to obtain the first marking point set corresponding to each shooting area at different time nodes, and the abnormal marking point set is statistically divided according to the same image position coordinates to obtain the second marking point set corresponding to the different position coordinates of each shooting area, and it is determined whether the number of abnormal marking points corresponding to the second marking point set exceeds the preset abnormal number. If it exceeds, the position of the shooting area corresponding to the number of abnormal marking points is marked to obtain the key monitoring position of the construction site, and the abnormal processing information corresponding to the key monitoring position is collected, and the abnormal processing information, shooting area and image abnormality type are respectively bound to the corresponding historical site image to obtain the abnormal processing image set corresponding to the key monitoring position.

[0118] Specifically, when performing anomaly analysis on each image in the historical scene image collection to obtain the shooting area and image anomaly type corresponding to each image, it is specifically used to: input each image in the historical scene image collection into a preset anomaly detection model for anomaly detection analysis to obtain the shooting area and image anomaly type corresponding to each image.

[0119] In the embodiment of the present application, each scene image is preprocessed, including size standardization, grayscale or color space conversion, to meet the model input requirements, and then the preprocessed scene image is input into a deep learning-based anomaly detection model, such as a convolutional neural network (CNN) or a recurrent neural network (RNN), for feature extraction and anomaly recognition, and finally, the model outputs anomaly detection results, including anomaly type and location information. In addition, other methods can also be used to implement anomaly detection, such as statistical-based methods or hybrid methods combining deep learning and rules, which are not limited here.

[0120] In the embodiment of the present application, the image anomaly type refers to the category of abnormal conditions identified by the model, such as abnormal operation of equipment, equipment failure, abnormal behavior of personnel, etc.

[0121] Step S14: Screening the current scene image set based on the key monitoring position to obtain the first priority image where the key monitoring position exists.

[0122] Specifically, a method based on deep learning is adopted. First, a deep learning model, such as a convolutional neural network (CNN), is trained to enable it to identify and locate key monitoring positions. Then, each image in the current on-site image set is input into the model to obtain the confidence and location information of the key monitoring positions output by the model. Finally, based on the results of the model output, the first priority images with confidence higher than the preset threshold are screened out. It is understandable that other methods can also be used to implement image screening, such as rule-based methods or hybrid methods combining deep learning and rules, which are not limited here.

[0123] Step S15: Determine whether there is an image anomaly in the first priority image. If so, perform similarity processing on the first priority image according to the abnormal processing image set to obtain construction information of the first priority image.

[0124] For the embodiment of the present application, based on the current on-site image set, a common-view on-site image that is adjacent to the time position node of the first priority image and has a key monitoring position is determined, three-dimensional simulation processing is performed based on the first priority image and the common-view on-site image to obtain an abnormal three-dimensional image with an image abnormality, abnormal coordinate information in the abnormal three-dimensional image and abnormal descriptor information corresponding to the abnormal coordinate information are determined, and multi-dimensional spatial data search is performed on the abnormal images in the abnormal processing image set based on the image feature points of the key monitoring position in the abnormal three-dimensional image to obtain a matching image set, and similarity analysis is performed between the first priority image and each abnormal image in the matching image set according to the abnormal coordinate information and the abnormal descriptor information to obtain similarity information, and a similarity mean is determined based on the similarity information, and it is judged whether the similarity mean satisfies a preset similarity range. If so, the target processing information, target area, and target abnormality type corresponding to the similarity mean in the abnormal processing image set are determined, and the target processing information, target area, and target abnormality type are respectively bound to the first priority image to obtain construction information of the first priority image.

[0125] Specifically, the description of the image feature points of the key monitoring positions in the abnormal three-dimensional image is to assign a specific and identifiable ID to the extracted feature points, which is usually related to the brightness of the surrounding images, which can be compared to issuing an ID number to each person. At the same time, the description of the feature point generally also includes an intensity value of the feature point, indicating the prominence of this feature. The higher the intensity, the less sensitive and more stable it is to the influence of ambient light, and the lower the intensity, the less reliable it is. Each extracted feature point can be expressed as: {(u,v),s,d}.

[0126] Where (u, v) represents the abnormal coordinates of the feature point, s is its intensity value, and d is the abnormal descriptor (usually a matrix of fixed size).

[0127] Specifically, the process of determining the abnormal coordinate information and abnormal descriptor information of the key monitoring position in the abnormal three-dimensional image includes: converting the abnormal three-dimensional image into an abnormal three-dimensional video according to a preset observation angle and a preset action change rule, and performing visual frame acquisition on the abnormal three-dimensional video to obtain an object visual frame, analyzing and processing the object visual frame to obtain an object map point, and determining the abnormal coordinate information and abnormal descriptor information corresponding to the image feature points of the key monitoring position in the abnormal three-dimensional image in different observation directions based on the object map points.

[0128] Among them, the preset observation angle includes the initial observation angle of the abnormal three-dimensional image and the final observation angle of the abnormal three-dimensional image. The preset action change rule is used to represent the change process rule of the abnormal three-dimensional image from the initial position to the final position. For example: when the abnormal three-dimensional image is tiled, its observation angle is defined as 90 degrees, that is, the observation is perpendicular to the abnormal three-dimensional image. At this time, the electronic device observes the image from left to right. The initial observation angle of the abnormal three-dimensional image is 0 degrees, and the final observation angle of the abnormal three-dimensional image is 180 degrees. The preset action change rule is based on the central axis of the abnormal three-dimensional image to rotate counterclockwise, so that the observation angle of the abnormal three-dimensional image changes from 0 degrees to 180 degrees.

[0129] Specifically, the first priority image is subjected to similarity analysis with each abnormal image in the matching image set according to the abnormal coordinate information and the abnormal descriptor information to obtain similarity information, including: the abnormal coordinate information is respectively matched with the corresponding coordinate position of each abnormal image in the matching image set to obtain matching descriptor information, and the matching descriptor information is used to represent the descriptor information corresponding to the abnormal coordinate information of each abnormal image in the matching image set. The descriptors in the abnormal descriptor information are respectively compared with the descriptors in the matching descriptor information to obtain similarity information.

[0130] Specifically, the descriptor matrix in the abnormal descriptor information is compared with the descriptor matrix in the matching descriptor information, and the matching degree in the two matrices is calculated to obtain similarity information. For example, the descriptor matrix in the abnormal descriptor information and the descriptor matrix in the matching descriptor information both have three rows and three columns of data, that is, nine groups of data, of which nine groups of data are the same, then the mean similarity of the two matrices is 100%. In the embodiment of the present application, the preset similarity range is that the mean similarity is not less than 86%.

[0131] Step S16: Determine whether there is a preset construction anomaly in the construction information. If yes, generate an alarm instruction and control the alarm device to output an alarm.

[0132] In the embodiment of the present application, the construction site image is obtained, and the on-site conditions at different time nodes in the preset construction site area are covered. Comprehensive monitoring of the construction site is ensured, and a rich data basis is provided for subsequent analysis. After the image is processed in a refined manner, the details of the site can be more clearly identified, providing high-quality image data for subsequent analysis. The processed construction site images are classified by time nodes to obtain a historical site image set and a current site image set, thereby achieving effective management of the construction site images. The historical images are analyzed for key abnormalities, and the key monitoring positions and abnormal processing image sets of the construction site can be accurately located, providing strong support for subsequent rapid response to abnormalities. The current image is filtered based on the key monitoring position to obtain the first priority image, thereby improving the efficiency of abnormal monitoring. By performing abnormal judgment on the first priority image, it is determined whether there is an abnormality such as occlusion that affects image monitoring at present. When there is, the first priority image is processed for similarity according to the abnormal processing image set, and the construction information corresponding to the occlusion abnormality in the first priority image is quickly obtained, and it is accurately judged whether the construction has a preset abnormality. Once an abnormality is detected, an alarm command is immediately generated to control the output of the alarm equipment, realizing real-time monitoring and timely early warning of the construction site, improving the effective and safe management level of the construction site, and thereby improving the efficiency of supervision of the construction site.

[0133] Further, it is determined whether there is an image anomaly in the first priority image, and then it also includes: if there is no image anomaly in the first priority image, then based on the current on-site image set, it is determined that there is no second priority image at the key monitoring position. It is determined whether there is an image anomaly in the second priority image. If there is an image anomaly, the monitoring anomaly position of the second priority image with the image anomaly is determined, and a UAV flight instruction is generated according to the monitoring anomaly position, and the UAV is controlled to fly from the initial position to the monitoring anomaly position for monitoring. The construction monitoring image sent by the UAV is collected, and it is determined whether there is a preset construction anomaly in the construction monitoring image. If so, the information to be processed and the type to be processed of the construction monitoring image are determined based on the construction anomaly standard, and the key monitoring position of the construction site and the abnormal processing image set corresponding to the key monitoring position are pre-updated according to the monitoring anomaly position, the information to be processed and the type to be processed, and the updated key monitoring position and the updated abnormal processing image set are obtained.

[0134] Furthermore, the key monitoring positions of the construction site and the abnormal processing image set corresponding to the key monitoring positions are pre-updated according to the monitoring abnormal positions, the information to be processed and the type to be processed, so as to obtain the updated key monitoring positions and the updated abnormal processing image set, and then it also includes: obtaining the reflection data of the image abnormality of the first priority image within the preset time, determining the abnormal frequency of the key monitoring position according to the reflection data, and determining whether the abnormal frequency exceeds the preset abnormal frequency. When the abnormal frequency does not exceed the preset abnormal frequency, the key monitoring position and the abnormal processing image set corresponding to the key monitoring position are eliminated and updated to obtain the updated key monitoring position and the abnormal processing image set.

[0135] In the embodiment of the present application, the preset abnormal frequency is a frequency set by the staff according to actual needs and is not limited here.

[0136] The above embodiment introduces a digital supervision management method applied to engineering projects from the perspective of method flow. The following embodiment introduces a digital supervision management system applied to engineering projects from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0137] The present application embodiment provides a digital supervision management system 20 applied to engineering projects, such as Figure 2 As shown, Figure 2 A schematic diagram of a digital supervision management system for an engineering project provided in an embodiment of the present application. The system 20 may specifically include:

[0138] An image acquisition module 21 is used to acquire construction site images, where the construction site images are site images corresponding to different time nodes in a preset construction site area;

[0139] The image processing module 22 is used to perform fine processing on the construction site image to obtain a processed construction site image;

[0140] An image classification module 23 is used to classify the processed construction site images according to time nodes to obtain a historical site image set and a current site image set;

[0141] The abnormality analysis module 24 is used to perform a key abnormality analysis on the historical on-site image set to obtain the key monitoring positions of the construction site and the abnormality processing image set corresponding to the key monitoring positions;

[0142] An image screening module 25 is used to screen the current on-site image set based on the key monitoring position to obtain a first priority image with a key monitoring position;

[0143] A similarity processing module 26 is used to determine whether there is an image anomaly in the first priority image. If so, similarity processing is performed on the first priority image according to the abnormal processing image set to obtain construction information of the first priority image.

[0144] The alarm control module 27 determines whether there is a preset construction anomaly in the construction information. If so, it generates an alarm instruction and controls the alarm device to output an alarm.

[0145] In a possible implementation of the embodiment of the present application, when the image processing module 22 performs fine processing on the construction site image to obtain the processed construction site image, it is specifically used to:

[0146] Perform edge filtering on the construction site image to obtain standard contour layers of different construction workers and construction equipment at the construction site and key contour layers of the construction workers' behavior details and the operation status details of the construction equipment at the construction site;

[0147] The standard contour layer is divided into a plurality of sub-regions, and a regional histogram corresponding to each sub-region and an overall histogram corresponding to the standard contour layer are collected, as well as a regional flatness corresponding to each sub-region and an overall flatness corresponding to the standard contour layer;

[0148] Determine the overall contrast curve and the overall brightness curve corresponding to the standard contour layer according to the overall histogram and the overall flatness;

[0149] Determine the regional contrast curve corresponding to each sub-region according to the sub-region histogram, and fuse the regional contrast curve with the overall contrast curve to obtain a fused contrast curve;

[0150] Determine the brightness curve adjustment weight corresponding to each sub-region according to the flatness of the sub-region, and based on the brightness curve adjustment weight, fuse the overall brightness curve with the fusion contrast curve to obtain a parameter adjustment curve;

[0151] Adjust each sub-region of the standard contour layer according to the parameter adjustment curve to obtain an adjusted standard contour layer;

[0152] The processed construction site image is obtained by fusing the adjusted standard contour layer with the key contour layer.

[0153] In another possible implementation of the embodiment of the present application, when the abnormality analysis module 24 performs a key abnormality analysis on the historical site image set to obtain a key monitoring position of the construction site and a set of abnormality processing images corresponding to the key monitoring position, it is specifically used to:

[0154] Perform anomaly analysis on each image in the historical on-site image collection to obtain the shooting area and image anomaly type corresponding to each image;

[0155] Summarize the abnormal marking points according to the shooting area to obtain the first marking point set corresponding to each shooting area at different time nodes;

[0156] Statistically dividing the abnormal mark point set according to the same image position coordinates to obtain a second mark point set corresponding to different position coordinates of each shooting area;

[0157] Determine whether the number of abnormal marking points corresponding to the second marking point set exceeds the preset abnormal number. If so, mark the position of the shooting area corresponding to the number of abnormal marking points to obtain the key monitoring position of the construction site;

[0158] The exception handling information corresponding to the key monitoring positions is collected, and the exception handling information, shooting area and image exception type are respectively bound to the corresponding historical on-site images to obtain the exception handling image set corresponding to the key monitoring positions.

[0159] In another possible implementation of the embodiment of the present application, when the similarity processing module 26 performs similarity processing on the first priority image according to the exception processing image set to obtain the construction information of the first priority image, it is specifically used to:

[0160] Determine, based on the current set of on-site images, a common-view on-site image that is adjacent to the time position node of the first priority image and has a key monitoring position;

[0161] Performing three-dimensional simulation processing based on the first priority image and the common viewing scene image to obtain an abnormal three-dimensional image with image abnormality;

[0162] Determine abnormal coordinate information in the abnormal three-dimensional image and abnormal descriptor information corresponding to the abnormal coordinate information;

[0163] Based on the image feature points of the key monitoring positions in the abnormal three-dimensional image, a multi-dimensional spatial data search is performed on the abnormal images in the abnormal processing image set to obtain a matching image set;

[0164] Performing similarity analysis on the first priority image and each abnormal image in the matching image set according to the abnormal coordinate information and the abnormal descriptor information to obtain similarity information;

[0165] Based on the similarity information, the similarity mean is determined, and it is judged whether the similarity mean satisfies the preset similarity range. If so, the target processing information, target area and target exception type corresponding to the similarity mean in the exception processing image set are determined, and the target processing information, target area and target exception type are respectively bound to the first priority image to obtain the construction information of the first priority image.

[0166] In another possible implementation of the embodiment of the present application, the system 20 further includes: an image determination module, a flight control module, a flight monitoring module, and a data update module, wherein:

[0167] An image determination module, configured to determine, when there is no image anomaly in the first priority image, that there is no second priority image at the key monitoring location based on the current on-site image set;

[0168] The flight control module is used to determine whether there is an image anomaly in the second priority image. If there is an image anomaly, the monitoring anomaly position of the second priority image where the image anomaly exists is determined, and a UAV flight instruction is generated according to the monitoring anomaly position to control the UAV to fly from the initial position to the monitoring anomaly position for monitoring;

[0169] The flight monitoring module is used to collect the construction monitoring images sent by the drone and determine whether there are preset construction anomalies in the construction monitoring images. If so, the information to be processed and the type to be processed of the construction monitoring images are determined based on the construction anomaly standard;

[0170] The data update module is used to pre-update the key monitoring locations of the construction site and the abnormal processing image set corresponding to the key monitoring locations according to the monitored abnormal locations, the information to be processed and the type to be processed, so as to obtain the updated key monitoring locations and the updated abnormal processing image set.

[0171] In another possible implementation of the embodiment of the present application, the system 20 further includes: a data acquisition module, a frequency determination module and a data elimination module, wherein:

[0172] A data acquisition module, used to acquire data reflecting that a first priority image has an image anomaly within a preset time;

[0173] A frequency determination module is used to determine the abnormal frequency occurring at the key monitoring location according to the reflected data, and to determine whether the abnormal frequency exceeds the preset abnormal frequency;

[0174] The data elimination module is used to eliminate and update the key monitoring position and the abnormal processing image set corresponding to the key monitoring position when the abnormal frequency does not exceed the preset abnormal frequency, so as to obtain the updated key monitoring position and abnormal processing image set.

[0175] In another possible implementation of the embodiment of the present application, when the abnormality analysis module 24 performs abnormality analysis on each image in the historical scene image set to obtain the shooting area and image abnormality type corresponding to each image, it is specifically used to:

[0176] Each image in the historical on-site image collection is input into the preset anomaly detection model for anomaly detection analysis to obtain the shooting area and image anomaly type corresponding to each image.

[0177] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the digital supervision management system 20 applied to engineering projects described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0178] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0179] Processor 301 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0180] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0181] The memory 303 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0182] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0183] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0184] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0185] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0186] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A digital supervision management method applied to engineering projects, characterized in that: include: Acquire construction site images, where the construction site images are site images corresponding to different time nodes in a preset construction site area; Performing fine processing on the construction site image to obtain a processed construction site image; Classify the processed construction site images according to time nodes to obtain a historical site image set and a current site image set; Performing key anomaly analysis on the historical on-site image set to obtain key monitoring locations of the construction site and an anomaly processing image set corresponding to the key monitoring locations; Filtering the current scene image set based on the key monitoring position to obtain a first priority image where the key monitoring position exists; Determine whether the first priority image has an image anomaly, and if so, perform similarity processing on the first priority image according to the abnormal processing image set to obtain construction information of the first priority image; It is determined whether there is a preset construction anomaly in the construction information. If so, an alarm instruction is generated to control the alarm device to output an alarm.

2. A digital supervision management method for engineering projects according to claim 1, characterized in that: The step of performing fine processing on the construction site image to obtain a processed construction site image includes: Performing edge filtering on the construction site image to obtain standard contour layers of different construction workers and construction equipment at the construction site and key contour layers of behavior details of the construction workers and operating status details of the construction equipment at the construction site; Divide the standard contour layer into a plurality of sub-regions, and collect a regional histogram corresponding to each sub-region and an overall histogram corresponding to the standard contour layer, as well as a regional flatness corresponding to each sub-region and an overall flatness corresponding to the standard contour layer; Determine an overall contrast curve and an overall brightness curve corresponding to the standard contour layer according to the overall histogram and the overall flatness; Determine a regional contrast curve corresponding to each sub-region according to the sub-region histogram, and fuse the regional contrast curve with the overall contrast curve to obtain a fused contrast curve; Determining a brightness curve adjustment weight corresponding to each sub-region according to the flatness of the sub-region, and fusing the overall brightness curve with the fused contrast curve based on the brightness curve adjustment weight to obtain a parameter adjustment curve; Adjusting each sub-region of the standard contour layer according to the parameter adjustment curve to obtain an adjusted standard contour layer; The processed construction site image is obtained by fusing the adjusted standard contour layer with the key contour layer.

3. A digital supervision management method for engineering projects according to claim 1, characterized in that: The performing of key abnormality analysis on the historical on-site image set to obtain key monitoring locations of the construction site and a set of abnormality processing images corresponding to the key monitoring locations includes: Performing an abnormality analysis on each image in the historical scene image set to obtain a shooting area and an image abnormality type corresponding to each image; Summarize the abnormal marking points according to the shooting areas to obtain a first marking point set corresponding to each shooting area at different time nodes; Statistically dividing the abnormal mark point set according to the same image position coordinates to obtain a second mark point set corresponding to different position coordinates of each shooting area; Determine whether the number of abnormal marking points corresponding to the second marking point set exceeds a preset abnormal number. If so, mark the position of the shooting area corresponding to the number of abnormal marking points to obtain a key monitoring position of the construction site; The exception handling information corresponding to the key monitoring position is collected, and the exception handling information, the shooting area and the image exception type are respectively bound to the corresponding historical scene images to obtain the exception handling image set corresponding to the key monitoring position.

4. A digital supervision management method for engineering projects according to claim 1, characterized in that: The performing similarity processing on the first priority image according to the exception processing image set to obtain the construction information of the first priority image includes: Determine, based on the current scene image set, a common view scene image that is adjacent to the time position node of the first priority image and has the key monitoring position; Performing three-dimensional simulation processing based on the first priority image and the common viewing scene image to obtain an abnormal three-dimensional image having the image abnormality; Determine abnormal coordinate information in the abnormal three-dimensional image and abnormal descriptor information corresponding to the abnormal coordinate information; Performing a multi-dimensional spatial data search on the abnormal images in the abnormal processing image set based on the image feature points of the key monitoring positions in the abnormal three-dimensional images to obtain a matching image set; Performing a similarity analysis between the first priority image and each abnormal image in the matching image set according to the abnormal coordinate information and the abnormal description sub-information to obtain similarity information; Based on the similarity information, a similarity mean is determined, and it is judged whether the similarity mean satisfies a preset similarity range. If so, the target processing information, target area and target exception type corresponding to the similarity mean in the exception processing image set are determined, and the target processing information, the target area and the target exception type are respectively bound to the first priority image to obtain the construction information of the first priority image.

5. A digital supervision management method for engineering projects according to claim 1, characterized in that: The determining whether the first priority image has an image anomaly further includes: If there is no image anomaly in the first priority image, determining based on the current on-site image set that there is no second priority image at the key monitoring location; Determine whether there is an image abnormality in the second priority image. If there is an image abnormality, determine a monitoring abnormality position where the image abnormality exists in the second priority image, generate a UAV flight instruction according to the monitoring abnormality position, and control the UAV to fly from an initial position to the monitoring abnormality position for monitoring; Collect the construction monitoring image sent by the drone, and determine whether the construction monitoring image has a preset construction anomaly, and if so, determine the to-be-processed information and the to-be-processed type of the construction monitoring image based on the construction anomaly standard; The key monitoring positions of the construction site and the abnormality processing image set corresponding to the key monitoring positions are pre-updated according to the monitored abnormality positions, the information to be processed and the type to be processed to obtain updated key monitoring positions and updated abnormality processing image set.

6. A digital supervision management method for engineering projects according to claim 5, characterized in that: The key monitoring positions of the construction site and the abnormal processing image set corresponding to the key monitoring positions are pre-updated according to the abnormal monitoring positions, the information to be processed and the type to be processed to obtain the updated key monitoring positions and the updated abnormal processing image set, and then further comprising: Acquire data reflecting that the first priority image has image abnormality within a preset time; Determine the abnormal frequency occurring at the key monitoring position according to the reflected data, and determine whether the abnormal frequency exceeds a preset abnormal frequency; When the abnormal frequency does not exceed the preset abnormal frequency, the key monitoring position and the abnormal processing image set corresponding to the key monitoring position are eliminated and updated to obtain an updated key monitoring position and abnormal processing image set.

7. A digital supervision management method for engineering projects according to claim 2, characterized in that: The performing of abnormality analysis on each image in the historical scene image set to obtain the shooting area and image abnormality type corresponding to each image includes: Each image in the historical scene image set is input into a preset anomaly detection model for anomaly detection analysis to obtain the shooting area and image anomaly type corresponding to each image.

8. A digital supervision management system applied to engineering projects, characterized in that: include: An image acquisition module is used to acquire construction site images, where the construction site images are site images corresponding to different time nodes in a preset construction site area; An image processing module is used to perform fine processing on the construction site image to obtain a processed construction site image; An image classification module is used to classify the processed construction site images according to time nodes to obtain a historical site image set and a current site image set; An anomaly analysis module, used to perform a key anomaly analysis on the historical on-site image set to obtain a key monitoring position of the construction site and an anomaly processing image set corresponding to the key monitoring position; An image screening module, configured to screen the current on-site image set based on the key monitoring position to obtain a first priority image that exists at the key monitoring position; a similarity processing module, configured to determine whether the first priority image has an image anomaly, and if so, perform similarity processing on the first priority image according to the anomaly processing image set to obtain construction information of the first priority image; The alarm control module is used to determine whether there is a preset construction anomaly in the construction information. If so, an alarm instruction is generated to control the alarm device to output an alarm.

9. An electronic device, characterized in that: It includes: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to: execute a digital supervision management method for engineering projects according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a digital supervision management method applied to an engineering project as described in any one of claims 1 to 7 is implemented.

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