Home decoration site supervision device based on AI large model

Abstract

The invention relates to a home decoration site supervision device based on an AI large model, and belongs to the field of artificial intelligence, and the device comprises a front-end data collection module which collects the video image data of a construction site in real time; the AI algorithm analysis processing module receives the video image data and performs intelligent analysis so as to comprehensively monitor a construction site and automatically identify potential problems; the early warning notification module automatically sends an early warning notification to related personnel when the AI algorithm analysis processing module detects an abnormality, and generates a processing list; and the remote monitoring management platform is used for checking construction site videos, receiving early warning notices and checking historical records, so that managers can know the conditions of the construction site in time and make corresponding processing decisions. According to the device, the supervision efficiency can be improved, the decoration quality is improved, the safety risk is reduced, and the management process is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of big data and artificial intelligence, and in particular relates to a home improvement site supervision device based on an AI big model. Background Art

[0002] With the rapid development of the modern home decoration industry, owners have increasing requirements for decoration quality and safety. However, the traditional way of supervision for home decoration mainly relies on manual on-site supervision, which is not only inefficient, but also difficult to fully cover every link in the construction process. Limited by personnel experience, fatigue and subjective judgment, manual supervision is often difficult to accurately identify irregular behaviors and quality problems in construction, such as construction workers cutting corners, not following safety regulations, using construction waste to backfill the bathroom, not hanging wire mesh at the junction of new and old walls, and not using a level for tile ceiling operations. These problems not only affect the quality of decoration, but may also bring serious safety hazards.

[0003] In addition, most of the existing home monitoring systems are based on traditional video monitoring technology. Although they can achieve real-time transmission of on-site images, they lack intelligent professional analysis and automatic warning functions, and cannot perform in-depth analysis and immediate response to video content. Even if some systems integrate simple image processing algorithms, they often cannot meet actual needs due to insufficient algorithm accuracy and high false alarm rates.

[0004] In recent years, with the rapid development of artificial intelligence and computer vision technology, solutions based on AI algorithm analysis large models have shown strong application potential in many fields. By training massive video data with deep learning algorithms, high-precision processing of tasks such as behavior recognition and object detection in complex scenes can be achieved. This technology provides new ideas and methods for on-site supervision of home improvement, that is, real-time monitoring and analysis of home improvement sites through AI video analysis large models, automatic identification of irregular behaviors and quality problems in construction, and triggering automatic early warning mechanisms to promptly notify relevant personnel for processing.

[0005] Therefore, how to apply artificial intelligence technology to on-site monitoring of home decoration and improve the intelligence of home decoration is a technical problem that needs to be solved at present. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a home improvement on-site supervision device based on an AI large model, which can improve supervision efficiency, improve decoration quality, reduce safety risks and optimize management processes.

[0007] The present invention proposes a home improvement on-site supervision device based on an AI large model, comprising: a front-end data acquisition module, an AI algorithm analysis and processing module, an early warning notification module and a remote monitoring management platform, wherein:

[0008] The front-end data acquisition module is deployed in a high-definition video surveillance device at a home improvement construction site to collect video image data of the construction site in real time;

[0009] The AI ​​algorithm analysis and processing module receives the video image data and performs intelligent analysis to comprehensively monitor the construction site and automatically identify potential problems, including: using a behavior object recognition algorithm to perform intelligent analysis on the video image data to identify whether construction personnel have irregular behaviors; using an object detection algorithm to perform intelligent analysis on the video image data to detect whether objects at the construction site do not meet the requirements; and using a quality assessment algorithm to assess construction quality.

[0010] The warning notification module is used to automatically send a warning notification to relevant personnel when the AI ​​algorithm analysis and processing module detects an abnormality, and generate a processing order;

[0011] The remote monitoring management platform is used to provide viewing of construction site videos, receiving early warning notifications, and viewing historical records, so that management personnel can be informed of the situation at the construction site in a timely manner and make corresponding processing decisions.

[0012] Furthermore, the front-end data acquisition module includes: a high-definition camera, a barcode scanner, an intelligent barcode scanner, a smart ruler and an electronic level.

[0013] Furthermore, the AI ​​algorithm analysis and processing module uses a behavior object recognition algorithm to perform intelligent analysis on the video image data to identify whether the construction personnel have any irregular behavior, including:

[0014] The AI ​​algorithm analysis and processing module adopts a model based on convolutional neural network and recurrent neural network or Transformer structure to capture the timing information in the video to identify the specific behavior of the construction workers, and adopts the object detection framework to locate and classify the objects in the video frames to identify the type and location of the objects and determine whether they meet the specifications.

[0015] Furthermore, the AI ​​algorithm analysis and processing module uses an object detection algorithm to perform intelligent analysis on the video image data to detect whether there are objects at the construction site that do not meet the requirements, including:

[0016] The AI ​​algorithm analysis and processing module is based on a convolutional neural network (CNN) structure, and detects specific objects in the construction site by fusion of multi-scale features and context information modeling of the video image data.

[0017] Furthermore, the AI ​​algorithm analysis and processing module uses a quality assessment algorithm to assess the construction quality, including:

[0018] The AI ​​algorithm analysis and processing module performs image preprocessing, feature extraction and quality assessment model construction on the video image data, and performs quantitative assessment of the construction quality.

[0019] Furthermore, relevant personnel make rectifications according to the processing sheet and submit rectification records to the remote monitoring management platform.

[0020] Furthermore, the warning notification module sends the warning notification to relevant personnel in at least one of the following ways: WeChat, SMS and APP.

[0021] Furthermore, the warning notification includes the anomaly type, location of occurrence, timestamp, and corresponding video clips or picture evidence.

[0022] Furthermore, the abnormal types include violations such as not wearing a safety helmet, problems with improper placement of materials, and quality issues such as wall flatness not meeting standards.

[0023] Furthermore, the remote monitoring management platform is also used to support data analysis, report generation and quality assessment weight configuration.

[0024] The beneficial effects of the present invention are as follows:

[0025] 1. Improve supervision efficiency: Through intelligent video analysis, comprehensive and real-time monitoring of the construction site is achieved, which significantly improves supervision efficiency and reduces the workload of manual inspections.

[0026] 2. Enhanced problem detection capabilities: The AI ​​algorithm’s precise recognition capabilities can promptly detect irregular behaviors and quality issues in construction, providing strong support for problem rectification.

[0027] 3. Improve decoration quality: Through timely warning and rectification, quality problems in the decoration process are effectively avoided, the overall decoration quality is improved, and the satisfaction of the owners is enhanced. And by generating a time microcosm of each construction stage and the entire decoration process, the service experience of the owners is improved.

[0028] 4. Reduce safety risks: Real-time monitoring of construction workers’ behavior helps to standardize construction operations, reduce the risk of safety accidents, and ensure the personal safety of construction workers.

[0029] 5. Optimize management processes: The use of remote monitoring and management platforms makes management processes more digital and transparent, facilitating management to make more scientific and reasonable decisions.

[0030] 6. Real-time and comprehensive: Compared with the traditional home improvement supervision method, the present invention realizes real-time and comprehensive monitoring of the home improvement site by introducing large model technology. This means that the supervisor can obtain the construction situation, progress, quality and other information on the site in real time through the system without going to the site in person, which greatly improves the efficiency and accuracy of supervision.

[0031] 7. Intelligence and automation: The present invention can interact with large models in real time to achieve automated and intelligent supervision. Through big data processing and machine learning algorithms, the system can automatically identify problems in construction, such as cutting corners, delayed progress, etc., and issue early warnings in a timely manner to help supervisors quickly respond and solve problems.

[0032] 8. Flexibility and scalability: The present invention fully considers the differences in requirements of different home improvement projects and has high flexibility and scalability. Supervisors can customize and expand the device according to the actual situation of the project to meet specific supervision needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0034] Figure 1 This is a structural diagram of a home improvement site supervision device based on an AI large model according to an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of a home improvement site supervision device based on an AI big model according to an embodiment of the present invention;

[0036] Figure 3 This is an implementation flow chart of a home improvement site supervision device based on an AI big model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0038] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0039] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0041] The technical terms involved in the present invention are explained below:

[0042] AI: Artificial Intelligence is an important driving force for a new round of scientific and technological revolution and industrial transformation. It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.

[0043] Large models refer to machine learning models with a large number of parameters and complex structures, which can process massive amounts of data and complete various complex tasks, such as natural language processing, computer vision, speech recognition, etc. These models usually have ultra-large-scale parameters (usually more than one billion) and super computing resources, which can process massive amounts of data and complete various complex tasks.

[0044] The present invention proposes a home improvement site supervision device based on an AI big model, which uses advanced AI algorithms to analyze the big model and combines high-definition video monitoring equipment to achieve intelligent monitoring and management of the home improvement site.

[0045] like Figure 1 and Figure 2 As shown, the home improvement on-site supervision device based on the AI ​​big model of the present invention includes: a front-end data acquisition module 100, an AI algorithm analysis and processing module 200, an early warning notification module 300 and a remote monitoring management platform 400.

[0046] Specifically, the front-end data acquisition module 100 is deployed in a high-definition video surveillance device at the home improvement construction site, which is used to collect video image data of the construction site in real time and transmit it to the AI ​​algorithm analysis and processing module 200. At the same time, intelligent auxiliary equipment such as intelligent scanners, intelligent rulers, and intelligent levels are provided to assist AI in analysis.

[0047] In an embodiment of the present invention, the front-end data acquisition module 100 includes: a high-definition camera, a barcode scanner, an intelligent barcode scanner, a smart ruler and an electronic level.

[0048] The AI ​​algorithm analysis and processing module 200 receives video image data and performs intelligent analysis to comprehensively monitor the construction site and automatically identify potential problems.

[0049] Specifically, the AI ​​algorithm analysis and processing module 200 has the required deep learning algorithm built in, and performs intelligent analysis on the video image data collected by the front-end data acquisition module 100. The AI ​​algorithm analysis and processing module 200 integrates the behavior + object recognition algorithm, the object detection algorithm and the quality assessment algorithm, and can realize the deep analysis and intelligent judgment of the video image data of the construction site. In particular, the behavior + object recognition algorithm can accurately identify the behavior of construction personnel and combine object detection to improve the accuracy and practicality of recognition. Through the comprehensive application of these algorithms, the AI ​​algorithm analysis and processing module 200 can comprehensively monitor the construction site and automatically identify potential problems.

[0050] (1) Use behavioral object recognition algorithms to intelligently analyze video image data to identify whether construction workers have any irregular behaviors, such as not wearing safety helmets when dismantling, or illegally working against the exterior wall without a safety rope.

[0051] The AI ​​algorithm analysis and processing module 200 adopts a model based on convolutional neural networks and recurrent neural networks or Transformer structures to capture timing information in the video to identify the specific behaviors of construction workers, and uses an object detection framework to locate and classify objects in the video frames to identify the type and location of the object and determine whether it complies with the specifications.

[0052] Specifically, the behavior + object recognition algorithm combines behavior recognition and object recognition technologies and is implemented through a deep learning framework (such as TensorFlow or PyTorch). The algorithm first preprocesses the video frames, including image scaling and normalization, and then feeds them into the behavior recognition network and object recognition network respectively. A large-scale home improvement video dataset is used for model training to ensure that the algorithm can cover behavior and object recognition in various complex scenes and lighting conditions.

[0053] Behavior recognition network: It uses models based on convolutional neural networks (CNN) and recurrent neural networks (RNN) or Transformer structures, such as I3D (Inflated 3D ConvNet) and SlowFast Networks, to capture the timing information in the video and identify the specific behaviors of construction workers, such as not wearing a safety helmet or not fastening a safety rope.

[0054] Object recognition network: Use mature object detection frameworks such as Faster R-CNN and YOLO series to locate and classify objects in video frames, identify the types and locations of objects such as materials and equipment, and determine whether they meet the specifications. For example, whether the material model and quantity match the purchase order, and whether the equipment is placed in a standardized manner.

[0055] (2) Use object detection algorithms to intelligently analyze video image data to detect whether there are objects on the construction site that do not meet the requirements. For example, the material delivery does not match the model and quantity on the purchase order, or the equipment is not placed in a standardized manner.

[0056] 1) Object Detection Algorithm:

[0057] Object detection algorithms are mainly used to detect specific objects in construction sites, such as materials, tools, etc. The algorithm is based on the convolutional neural network (CNN) structure and improves the accuracy and robustness of detection through multi-scale feature fusion and context information modeling.

[0058] The AI ​​algorithm analysis and processing module 200 is based on a convolutional neural network (CNN) structure, and detects specific objects in the construction site through multi-scale feature fusion and context information modeling of video image data.

[0059] In an embodiment of the present invention, advanced object detection models such as RetinaNet and Mask R-CNN are selected. These models not only provide high-precision detection results, but also can generate accurate bounding boxes and masks of objects.

[0060] The model is trained on targeted data sets to ensure that it can accurately identify various materials and equipment commonly found on home improvement sites.

[0061] Post-processing techniques such as non-maximum suppression (NMS) are used to eliminate redundant detection frames and improve detection efficiency.

[0062] (3) Use quality assessment algorithms to evaluate construction quality, such as wall flatness and ceiling levelness.

[0063] The AI ​​algorithm analysis and processing module 200 performs image preprocessing, feature extraction and quality assessment model construction on the video image data, and performs quantitative assessment of the construction quality.

[0064] Specifically, the quality assessment algorithm uses image processing technology to quantitatively assess the construction quality, such as wall flatness, ceiling levelness, etc. The algorithm may include steps such as image preprocessing, feature extraction, and quality assessment model construction.

[0065] The AI ​​algorithm analysis and processing module 200 uses feature extraction algorithms such as SIFT and SURF to extract feature points of walls, ceilings and other areas from video frames. Build a quality assessment model based on machine learning or deep learning, such as using algorithms such as random forests and gradient boosted trees (GBDT) to perform regression analysis on features, or using convolutional neural networks (CNN) for end-to-end quality assessment. Design reasonable evaluation indicators, such as mean square error (MSE), root mean square error (RMSE), etc., to quantify the accuracy of the evaluation results.

[0066] The early warning notification module 300 is used to automatically send an early warning notification to relevant personnel when the AI ​​algorithm analysis and processing module 200 detects an abnormality, and generate a processing sheet. The relevant personnel are, for example, project managers, supervisors, owners, etc. The relevant personnel make rectifications according to the processing sheet and submit rectification records to the remote monitoring management platform 400.

[0067] The processing order record includes: time, problem, screenshot, video playback, processing personnel, processing status (pending, processed), processing description, processing certificate (file, supports jpg, pdf, zip), etc. After the relevant personnel complete the rectification record, they shall submit it to the monitoring management platform.

[0068] It should be noted that all information aggregation and management are the responsibility of the monitoring management platform.

[0069] In an embodiment of the present invention, the warning notification module 300 sends the warning notification to relevant personnel in at least one of the following ways: WeChat, SMS, APP, etc.

[0070] The early warning notification includes the type of anomaly, location of occurrence, timestamp, and corresponding video clips or picture evidence, so that relevant personnel can respond and handle it quickly.

[0071] Among them, the abnormal types include violations such as not wearing a safety helmet, problems with improper placement of materials, and quality issues such as wall flatness not meeting standards.

[0072] The remote monitoring management platform 400 provides a user-friendly remote monitoring management platform 400. Relevant personnel can use the platform to view construction site videos, receive early warning notifications, view historical records, and generate data analysis reports, so that managers can be informed of the situation on the construction site in a timely manner and make corresponding processing decisions, providing managers with efficient and convenient supervision tools.

[0073] In addition, the remote monitoring management platform 400 is also used to support data analysis, report generation, and quality assessment weight configuration, etc., to help managers better understand the situation at the construction site and make corresponding decisions.

[0074] The remote monitoring management platform 400 provides a comprehensive remote monitoring management platform 400 that supports real-time video viewing, early warning notification reception, historical record query, and data analysis report generation.

[0075] The present invention provides an intelligent home improvement on-site supervision solution by integrating advanced artificial intelligence (AI) algorithm analysis large model and high-definition video monitoring technology.

[0076] like Figure 3 As shown, taking a residential home improvement project as an example, the home improvement on-site supervision device based on the AI ​​large model of the present invention is described:

[0077] 1. First, high-definition video surveillance equipment and auxiliary intelligent equipment such as intelligent level and intelligent barcode scanner were deployed in key areas of the construction site to ensure full coverage of the construction process. The AI ​​algorithm analysis and processing module 200 was used to perform intelligent analysis, testing and training on the collected video images, measurements and other data to ensure the accuracy of the algorithm.

[0078] 2. During the construction process, the AI ​​algorithm analysis and processing module 200 successfully identified the violation of a construction worker not wearing a safety helmet. The behavior + object recognition algorithm analyzed the construction worker's behavior in the video image and found that the worker was not wearing a safety helmet, and immediately triggered the early warning mechanism. At the same time, the object detection algorithm also detected that there was a pile of materials placed irregularly at the construction site, which may affect the construction progress and safety. The quality assessment algorithm conducted a quality assessment on the completed wall plastering and found that the wall flatness did not meet the standard.

[0079] 3. The early warning notification module 300 then sent an early warning notification to the project manager and supervisor, including the violation of not wearing a safety helmet, the problem of improper material placement, and the quality problem of substandard wall flatness, and attached corresponding video clips or picture evidence. After receiving the notification, the project manager and supervisor quickly made remote calls through the cloud docking device, rushed to the site, corrected the violation, and adjusted the material placement.

[0080] 4. The owners also paid real-time attention to the construction progress and quality through the remote monitoring management platform 400. They received early warning notifications and viewed the corresponding video clips or picture evidence. The owners are more assured and satisfied with the decoration process because they can understand the situation on the construction site in a timely manner and deal with potential problems in a timely manner. This embodiment fully demonstrates the remarkable effects of the technical solution of the present invention in improving the quality of home decoration, strengthening on-site supervision, and improving management efficiency.

[0081] The home improvement site supervision device based on the AI ​​big model according to the embodiment of the present invention can be applied to the following scenarios:

[0082] The present invention is suitable for application scenarios such as home decoration projects, internal management of decoration companies, supervision of building materials suppliers, and supervision by government regulatory departments. It provides intelligent home decoration on-site supervision tools for all relevant parties, promotes the intelligent upgrade of the home decoration industry, and provides owners with a better-quality and safer home decoration service experience.

[0083] The home decoration on-site supervision device based on the AI ​​large model of the embodiment of the present invention can provide owners with a worry-free decoration service experience, improve the construction service quality for decoration companies, improve the supervision efficiency for government regulatory departments, and provide solutions for smart home / AI application system integration manufacturers. The present invention can expand the AI ​​company model landing scenarios and expand the business scope of the VisionLink company's VisionLink + AI applications and solutions.

[0084] The home improvement site supervision device based on the AI ​​big model of the present invention solves the pain points of low efficiency, incomplete coverage, and difficulty in timely detection of problems in the traditional home improvement supervision method. The present invention combines the technical advantages of the existing AI algorithm analysis big model to achieve real-time monitoring and analysis of the home improvement site, automatically identify irregular behaviors and quality problems in construction, and trigger an automatic early warning mechanism to promptly notify relevant personnel to handle them, solving the shortcomings of the traditional supervision method, improving the quality of home improvement and management efficiency, and reducing the probability of irregular behaviors and quality problems, thereby protecting the rights and interests of owners and improving the service level of the overall home improvement industry, bringing intelligent upgrades to the home improvement industry.

[0085] The home improvement site supervision device based on the AI ​​large model of the present invention further provides intelligent home improvement site supervision tools for multiple fields such as owners, decoration companies, building material suppliers, government regulatory departments, and smart home system integration, and promotes the intelligent upgrade of the home improvement industry. All relevant parties can more conveniently and efficiently realize remote monitoring and management of the home improvement site, improve overall work efficiency and service quality, help improve the service level of the overall home improvement industry, and protect the rights and interests of owners.

[0086] The present invention realizes comprehensive perception and real-time analysis of the site environment, construction progress, worker operations, etc. by constructing a large model of the home improvement site, and designs a set of intelligent supervision devices that can interact with the large model in real time to realize automated and intelligent supervision of the home improvement site.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the above embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A home improvement site supervision device based on AI big model, characterized in that: include: Front-end data acquisition module, AI algorithm analysis and processing module, early warning notification module and remote monitoring management platform, among which, The front-end data acquisition module is deployed in a high-definition video surveillance device at a home improvement construction site to collect video image data of the construction site in real time; The AI ​​algorithm analysis and processing module receives the video image data and performs intelligent analysis to comprehensively monitor the construction site and automatically identify potential problems, including: using a behavior object recognition algorithm to perform intelligent analysis on the video image data to identify whether construction personnel have irregular behaviors; using an object detection algorithm to perform intelligent analysis on the video image data to detect whether objects at the construction site do not meet the requirements; and using a quality assessment algorithm to assess construction quality. The warning notification module is used to automatically send a warning notification to relevant personnel when the AI ​​algorithm analysis and processing module detects an abnormality, and generate a processing order; The remote monitoring management platform is used to provide viewing of construction site videos, receiving early warning notifications, and viewing historical records, so that management personnel can be informed of the situation at the construction site in a timely manner and make corresponding processing decisions.

2. According to claim 1, a home improvement site supervision device based on an AI large model is characterized in that: The front-end data acquisition module includes: a high-definition camera, a barcode scanner, an intelligent barcode scanner, a smart ruler and an electronic level.

3. The home improvement site supervision device based on AI big model according to claim 1 is characterized in that: The AI ​​algorithm analysis and processing module uses a behavior object recognition algorithm to perform intelligent analysis on the video image data to identify whether the construction personnel have irregular behaviors, including: The AI ​​algorithm analysis and processing module adopts a model based on convolutional neural network and recurrent neural network or Transformer structure to capture the timing information in the video to identify the specific behavior of the construction workers, and adopts the object detection framework to locate and classify the objects in the video frames to identify the type and location of the objects and determine whether they meet the specifications.

4. The home improvement site supervision device based on AI big model according to claim 1 is characterized in that: The AI ​​algorithm analysis and processing module uses an object detection algorithm to perform intelligent analysis on the video image data to detect whether there are objects at the construction site that do not meet the requirements, including: The AI ​​algorithm analysis and processing module is based on a convolutional neural network (CNN) structure, and detects specific objects in the construction site by fusion of multi-scale features and context information modeling of the video image data.

5. The home improvement site supervision device based on AI big model according to claim 1 is characterized in that: The AI ​​algorithm analysis and processing module uses a quality assessment algorithm to assess the construction quality, including: The AI ​​algorithm analysis and processing module performs image preprocessing, feature extraction and quality assessment model construction on the video image data, and performs quantitative assessment of the construction quality.

6. The home improvement site supervision device based on AI big model according to claim 1 is characterized in that: Relevant personnel shall make rectifications according to the processing sheet and submit rectification records to the remote monitoring management platform.

7. The home improvement site supervision device based on AI big model according to claim 1 is characterized in that: The early warning notification module sends the early warning notification to relevant personnel in at least one of the following ways: WeChat, SMS and APP.

8. The home improvement site supervision device based on AI big model according to claim 1 is characterized in that: The warning notification includes the anomaly type, location of occurrence, timestamp, and corresponding video clips or picture evidence.

9. The home improvement site supervision device based on AI big model according to claim 8, characterized in that: The types of abnormalities mentioned include violations such as not wearing a safety helmet, problems with improper placement of materials, and quality issues such as wall flatness not meeting standards.

10. The home improvement site supervision device based on AI big model according to claim 1, characterized in that: The remote monitoring management platform is also used to support data analysis, report generation and quality assessment weight configuration.

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