Construction operation standardization process detection system and method based on algorithm model
The algorithm-based standardized construction operation process detection system solves the problems of complexity and lag in existing construction site management systems, enabling refined management and rapid updates of the construction process, and improving construction safety and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT BUILDING MATERIALS TECH CO LTD
- Filing Date
- 2026-03-12
- Publication Date
- 2026-06-19
AI Technical Summary
Existing construction site management systems are unable to achieve precise control over complex and dynamic work processes throughout the entire process. They lack dynamic comparison and evaluation of work behaviors and preset standardized processes. Furthermore, the systems are complex to deploy, difficult to update, and have slow response times, making it impossible to effectively prevent work risks.
A standardized construction operation process detection system based on algorithm models is adopted. Through process node management, visual arrangement, dynamic monitoring tag library and multi-strategy AI monitoring, the system realizes the visual arrangement and dynamic monitoring of the operation process. It supports small sample training to quickly update the tag library, and performs multi-level alarm and closed-loop handling through real-time feedback and linkage response mechanism.
It has achieved a leap from macro-level extensive management to micro-level precise control of complex construction processes, reduced technical thresholds and deployment cycles, improved the level of operational standardization and proactive safety prevention and control capabilities, and shortened the response and handling time for safety hazards.
Smart Images
Figure CN122243688A_ABST
Abstract
Description
Technical Field
[0001] This document relates to the field of construction inspection technology, and in particular to a standardized process inspection system and method for construction operations based on an algorithm model. Background Technology
[0002] In construction engineering, industrial assembly, equipment maintenance, and other construction operations, the standardization of work processes and the management of safety and compliance are crucial to ensuring project quality and construction safety. For a long time, construction site management has relied primarily on manual inspections, paper records, or video surveillance based on fixed cameras. These methods generally suffer from low monitoring efficiency, incomplete coverage, delayed response, and strong subjectivity, making it difficult to achieve comprehensive and meticulous management of complex and dynamic work processes.
[0003] In recent years, with the development of artificial intelligence and computer vision technologies, some technical solutions have attempted to introduce AI technology into construction site management. For example, deploying smart cameras to monitor specific scenarios such as safety helmet detection and area intrusion identification, or collecting equipment status data based on sensor networks. However, existing technologies mostly focus on static recognition of single scenarios or local states, lacking the ability to monitor the continuity and logic of the entire work process, and unable to dynamically compare and evaluate work behavior against pre-set standardized procedures. Especially for complex work processes involving multiple procedures, multi-person collaboration, and multi-device interaction, existing systems struggle to effectively break down procedures, monitor nodes, and determine process compliance.
[0004] Furthermore, existing visual recognition systems typically rely on pre-trained, fixed models, making it difficult to update label libraries and adapt to the monitoring needs of different industries, processes, and standards. In terms of process modeling and system configuration, these often require professionals to complete them through code or complex configuration interfaces, resulting in high barriers to entry, long deployment cycles, and difficulty in achieving rapid and flexible process adaptation and strategy adjustments. Regarding anomaly response mechanisms, existing systems are mostly limited to simple alarms, lacking hierarchical response, closed-loop handling, and collaborative linkage capabilities tied to work process nodes, leading to lagging management measures and an inability to effectively prevent operational risks.
[0005] Therefore, there is an urgent need for a standardized construction operation process detection system that can deeply integrate process management concepts with AI visual recognition technology, support visual arrangement of work processes, dynamic monitoring of nodes, flexible expansion of tag libraries, and has intelligent early warning and closed-loop response capabilities, so as to improve the refinement and intelligence of construction management and fundamentally ensure the safety and quality of operations. Summary of the Invention
[0006] This specification provides one or more embodiments of a standardized construction operation process detection system based on an algorithm model, including: Process node management module: used to break down a work process into multiple independently configurable process nodes, and bind at least one monitoring tag and monitoring strategy to each node; Visual process orchestration module: Used to sort, associate and configure process nodes by dragging and dropping through a graphical user interface, and generate process logic diagrams in real time; Dynamic monitoring tag library module: used to store a standardized set of monitoring tags based on video AI recognition, and dynamically expand and update the tag library through a few-sample training mechanism; Multi-strategy AI monitoring module: Used to perform AI analysis on real-time video streams or on-site captured images based on the monitoring strategies configured for the nodes, and to determine whether the node operations comply with preset specifications; Real-time feedback and linkage response module: used to summarize the detection results of each node, output anomaly reports, and trigger multi-level alarms and closed-loop processing instructions according to the anomaly level.
[0007] Furthermore, the process node management module is specifically used to: provide a specific operation task in the work process corresponding to each process node, and the node attributes include node name, node description, expected time consumption, required tools or equipment, and a list of bound monitoring tags.
[0008] Furthermore, the visualization process orchestration module is specifically used to: configure the logical relationships between nodes, including sequential execution, parallel execution and conditional branching, and generate executable process monitoring logic; In the graphical user interface, each process node is represented by a graphical element. Users can build flowcharts by dragging and dropping graphical elements and connect them to represent the logical relationships between nodes.
[0009] Furthermore, in the dynamic monitoring tag library module, the method for adding new tags is as follows: Users upload sample data containing new tags, and the system completes model optimization and tag entry into the database within a set period based on the few-shot learning algorithm; The monitoring tags include tags for personnel protection, tags for operational behavior, and tags for environmental status.
[0010] Furthermore, the multi-strategy AI monitoring module is specifically used for: Real-time video analysis is performed on high-risk, continuous operation nodes through continuous video stream monitoring mode; By using image capture and analysis, intermittent and dispersed work nodes can be identified on a timed or triggered basis. The specific monitoring method of the continuous video stream monitoring mode is as follows: continuous frame analysis technology is used to identify the action sequence of the operator in order to determine whether the operation sequence conforms to the standard.
[0011] Furthermore, the real-time feedback and linkage response module is specifically used to: automatically trigger at least one of the following alarm methods according to the type and level of the anomaly: APP push, SMS notification, on-site audible and visual alarm, and attach screenshots or GIFs of abnormal evidence; The abnormality levels are classified into general violations and major hidden dangers based on the type and frequency of the abnormality. Major hidden dangers trigger on-site audible and visual alarms and simultaneously notify management personnel.
[0012] Furthermore, the system also includes a statistical analysis module, used for: Generate full-process compliance rate reports, anomaly distribution heatmaps, and historical traceability reports, and export and visualize the data in multiple dimensions; Generate reports on the compliance rate and anomaly distribution of the entire job process, and filter and export data by time, job type, and node type.
[0013] Furthermore, the system also includes a job scenario configuration module, which is used to predefine standard job process templates according to different job types, and users can adjust process nodes and configure monitoring strategies based on the predefined standard job process templates; The operation scenario configuration module supports multiple operation types, including building construction, industrial assembly, and equipment maintenance, and provides preset process nodes and tag libraries for each type.
[0014] This specification provides one or more embodiments of a standardized construction operation process detection method based on an algorithm model, including: S1. The workflow is broken down into multiple independently configurable workflow nodes, and each node is bound to at least one monitoring tag and monitoring strategy; S2. Through the visual process orchestration interface, process nodes can be sorted, associated and configured by dragging and dropping to generate a process logic diagram; S3. Based on the dynamic monitoring tag library, the tag library is dynamically expanded and updated through a small sample training mechanism; S4. Based on the monitoring strategy configured for the node, use the multi-strategy AI monitoring engine to perform AI analysis on the real-time video stream or captured images to determine whether the node operation conforms to the preset specifications. S5. Summarize the detection results of each node, output an anomaly report, and trigger multi-level alarms and closed-loop processing instructions according to the anomaly level.
[0015] This specification provides one or more embodiments of a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the above-described algorithm-based standardized process detection method for construction operations.
[0016] By employing embodiments of this invention, the work process is broken down into independently configurable monitoring nodes, and a visual orchestration and dynamic tag library is constructed, achieving a leap from macro-level extensive management to micro-level precise control of complex construction processes. The intuitive drag-and-drop interface allows users to quickly model and configure complex processes without professional programming, significantly reducing the technical threshold and deployment cycle. The dynamic monitoring tag library supports rapid adaptation to new specifications and equipment through small-sample training, ensuring continuous expansion and iteration of the system's recognition capabilities. Based on the work scenario, it intelligently integrates continuous video stream analysis and image capture recognition modes, optimizing resource consumption while ensuring real-time monitoring of key links. Through AI recognition, anomaly detection, tiered alarms, evidence push, and closed-loop handling, the response and handling time for safety hazards can be significantly shortened, fundamentally improving the level of work standardization, proactive safety prevention and control capabilities, and overall management efficiency.
[0017] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A schematic diagram illustrating the composition of a standardized construction operation process detection system based on an algorithm model, provided for one or more embodiments of this specification; Figure 2 This document provides a flowchart of a standardized construction operation process detection method based on an algorithm model, which is used in one or more embodiments of this specification. Detailed Implementation
[0020] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.
[0021] System Implementation Examples According to embodiments of the present invention, a standardized construction operation process detection system based on an algorithm model is provided. Figure 1 A schematic diagram illustrating the composition of a standardized construction operation process detection system based on an algorithm model, provided for one or more embodiments of this specification, is shown below. Figure 1 As shown, the standardized construction operation process detection system based on an algorithm model according to an embodiment of the present invention specifically includes: Process Node Management Module 10: This module is used to break down a work process into multiple independently configurable process nodes and bind at least one monitoring tag and monitoring strategy to each node.
[0022] Each process node corresponds to a specific operational task within the work process. For example, in rebar tying operations, rebar positioning, spacing tying, and node reinforcement can be defined as three independent process nodes. Each node is configured with attribute parameters, including node name and description, expected standard time consumption, a list of specific tools or equipment required, and a list of monitoring tags bound to that node. These tags are sourced from a dynamic monitoring tag library, covering multiple dimensions such as personnel protection, operational behavior, and environmental conditions.
[0023] Visualized process orchestration module 12: It is used to sort, associate and configure process nodes by dragging and dropping through a graphical user interface, and generate process logic diagrams in real time.
[0024] In the graphical user interface, each split process node is abstracted as an independent graphical element. Users do not need any programming background; they can simply drag and drop nodes to designated positions on the canvas and intuitively define their logical execution relationships through connections between nodes. These connections not only represent the sequence of processes but also support logical relationship configuration: sequential execution means that the next node can only start after the previous node is completed, suitable for work steps with strict dependencies; parallel execution means that multiple nodes can be monitored simultaneously, suitable for cross-operations or collaborative construction scenarios; conditional branching allows dynamic decision-making of subsequent execution paths based on the detection results of the previous node, such as automatically jumping to the rectification and review process when a node fails a detection. Each time a connection or node adjustment is completed, the corresponding executable process monitoring logic is generated instantly, without the need for manual rule script writing.
[0025] Dynamic monitoring tag library module 14: Used to store a standardized set of monitoring tags based on video AI recognition, and dynamically expands and updates the tag library through a small sample training mechanism.
[0026] A few-shot training mechanism is introduced to dynamically expand and continuously update the tag library. When new protective equipment, special process requirements, or updated operating procedures appear at the work site, users do not need to collect massive amounts of training data. They only need to upload a small number of representative sample images, and the model optimization, feature extraction, and tag addition can be completed within 1 to 3 days based on the few-shot learning algorithm, shortening the cycle of several weeks or even months required for traditional AI model iteration. The module's built-in monitoring tags cover the three core dimensions of construction operation compliance judgment: personnel protection tags are used to identify the wearing status of personal protective equipment such as safety helmets, safety belts, protective gloves, and goggles; operation behavior tags are used to judge dynamic behaviors such as the sequence of work steps, equipment operation posture, and tool usage standards; and environmental status tags focus on on-site environmental elements such as the setting of warning signs in the work area, the status of equipment operation indicator lights, and the integrity of temporary barriers.
[0027] Multi-strategy AI monitoring module 16: Used to perform AI analysis on real-time video streams or on-site captured images based on the monitoring strategies configured for the nodes, and to determine whether the node operations comply with preset specifications.
[0028] For high-risk, continuous work nodes such as high-altitude operations, hot work, and large equipment operations, a continuous video stream monitoring mode is activated. High-definition cameras deployed on-site collect video stream data in real time. An AI engine performs continuous frame analysis of the content at a frequency of multiple frames per second. This not only identifies the state of a single frame at a given moment but, more importantly, through temporal modeling of the action sequences between adjacent frames, fully reconstructs the operator's operational trajectory and behavioral logic. This allows for accurate judgment of whether certain operational steps with strict sequential requirements are compliant. For example, in electrical equipment maintenance nodes, semantic analysis of actions in continuous footage can identify whether operators are performing actions without verifying the power supply, or other sequentially incorrect behaviors. For intermittent, dispersed work nodes such as equipment inspection, temporary maintenance, and material acceptance, an image capture and analysis mode is adopted. Operators take photos of key on-site conditions using mobile devices such as smartphones and tablets and upload them. AI recognition is performed on the images in real time. Simultaneously, automatic capture tasks are triggered at preset time intervals for periodic sampling and analysis of periodic work processes. The two monitoring modes can be flexibly combined and switched as needed within the same process, based on factors such as network bandwidth, equipment computing resources, and node risk levels.
[0029] Real-time feedback and linkage response module 18: Used to summarize the detection results of each node, output anomaly reports, and trigger multi-level alarms and closed-loop processing instructions according to the anomaly level.
[0030] Once the monitoring data from each process node is aggregated into this module, the automation level is first determined based on the type and frequency of the anomaly: occasional, low-risk violations such as not wearing gloves or improper tool placement are marked as general violations; behaviors with direct safety threats, such as not wearing safety belts, operating special equipment without a license, or seriously reversing procedures, are classified as major hazards. Based on this classification mechanism, differentiated linkage response strategies are intelligently matched through real-time feedback and linkage response modules: general violations are notified to the responsible person via APP workbench push notifications, SMS summary reminders, etc., prompting them to correct the violation immediately, along with screenshots or animated GIFs as evidence of the violation, ensuring accurate information transmission; major hazards, in addition to the above push notifications, simultaneously trigger audible and visual alarm devices deployed in the work area, using audiovisual signals to warn on-site personnel to immediately stop work and take shelter, and push the alarm information and complete evidence chain to the terminals of management personnel such as the safety director and project manager in real time.
[0031] The system also includes a statistical analysis module, which generates full-process compliance rate reports, anomaly distribution heatmaps, and historical traceability reports, and exports and visualizes the data in multiple dimensions; it generates full-process compliance rate and anomaly distribution reports, and filters and exports data by time, job type, and node type.
[0032] The beneficial effects of this invention are as follows: This invention achieves a leap from macro-level extensive management to micro-level precise control of complex construction processes by breaking down the work process into independently configurable monitoring nodes and constructing a visual orchestration and dynamic tag library. With an intuitive drag-and-drop interface, users can quickly complete the modeling and strategy configuration of complex processes without professional programming, greatly reducing the technical threshold and deployment cycle. The dynamic monitoring tag library supports rapid adaptation to new specifications and equipment through small-sample training, ensuring the continuous expansion and iteration of the system's recognition capabilities. It intelligently integrates video stream continuous analysis and image capture recognition modes according to the work scenario, optimizing resource consumption while ensuring real-time monitoring of key links. Through AI recognition, anomaly detection, tiered alarms, evidence push, and closed-loop handling, the response and handling time for safety hazards can be significantly shortened, fundamentally improving the level of work standardization, proactive safety prevention and control capabilities, and overall management efficiency.
[0033] Method Implementation Examples According to embodiments of the present invention, a method for detecting standardized construction operation processes based on an algorithm model is provided. Figure 2 A flowchart illustrating a standardized construction operation process detection method based on an algorithm model, provided for one or more embodiments of this specification, is shown below. Figure 2 As shown, the standardized construction operation process detection method based on the algorithm model according to an embodiment of the present invention specifically includes: S1. The workflow is broken down into multiple independently configurable workflow nodes, and each node is bound to at least one monitoring tag and monitoring strategy; S2. Through the visual process orchestration interface, process nodes can be sorted, associated and configured by dragging and dropping to generate a process logic diagram; S3. Based on the dynamic monitoring tag library, the tag library is dynamically expanded and updated through a small sample training mechanism; S4. Based on the monitoring strategy configured for the node, use the multi-strategy AI monitoring engine to perform AI analysis on the real-time video stream or captured images to determine whether the node operation conforms to the preset specifications. S5. Summarize the detection results of each node, output an anomaly report, and trigger multi-level alarms and closed-loop processing instructions according to the anomaly level.
[0034] The embodiments of the present invention are method embodiments corresponding to the system embodiments described above. The specific operations of each step can be understood by referring to the description of the system embodiments, and will not be repeated here.
[0035] Device Example 1 This invention provides a computer-readable storage medium storing an implementation program for information transmission. When executed by a processor, the program performs the following method steps: S1. The workflow is broken down into multiple independently configurable workflow nodes, and each node is bound to at least one monitoring tag and monitoring strategy; S2. Through the visual process orchestration interface, process nodes can be sorted, associated and configured by dragging and dropping to generate a process logic diagram; S3. Based on the dynamic monitoring tag library, the tag library is dynamically expanded and updated through a small sample training mechanism; S4. Based on the monitoring strategy configured for the node, use the multi-strategy AI monitoring engine to perform AI analysis on the real-time video stream or captured images to determine whether the node operation conforms to the preset specifications. S5. Summarize the detection results of each node, output an anomaly report, and trigger multi-level alarms and closed-loop processing instructions according to the anomaly level.
[0036] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.
[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A standardized construction operation process detection system based on an algorithm model, characterized in that, include: Process node management module: used to break down a work process into multiple independently configurable process nodes, and bind at least one monitoring tag and monitoring strategy to each node; Visual process orchestration module: Used to sort, associate and configure process nodes by dragging and dropping through a graphical user interface, and generate process logic diagrams in real time; Dynamic monitoring tag library module: used to store a standardized set of monitoring tags based on video AI recognition, and dynamically expand and update the tag library through a few-sample training mechanism; Multi-strategy AI monitoring module: Used to perform AI analysis on real-time video streams or on-site captured images based on the monitoring strategies configured for the nodes, and to determine whether the node operations comply with preset specifications; Real-time feedback and linkage response module: used to summarize the detection results of each node, output anomaly reports, and trigger multi-level alarms and closed-loop processing instructions according to the anomaly level.
2. The method according to claim 1, characterized in that, The process node management module is specifically used to: provide a specific operation task in the work process corresponding to each process node. The node attributes include node name, node description, expected time, required tools or equipment, and a list of bound monitoring tags.
3. The method according to claim 1, characterized in that, The visualization process orchestration module is specifically used to: configure the logical relationships between nodes, including sequential execution, parallel execution and conditional branching, and generate executable process monitoring logic; In the graphical user interface, each process node is represented by a graphical element. Users can build flowcharts by dragging and dropping graphical elements and connect them to represent the logical relationships between nodes.
4. The method according to claim 1, characterized in that, In the dynamic monitoring tag library module, the method for adding new tags is as follows: Users upload sample data containing new tags, and the system completes model optimization and tag entry into the database within a set period based on the few-shot learning algorithm; The monitoring tags include tags for personnel protection, tags for operational behavior, and tags for environmental status.
5. The method according to claim 1, characterized in that, The multi-strategy AI monitoring module is specifically used for: Real-time video analysis is performed on high-risk, continuous operation nodes through continuous video stream monitoring mode; By using image capture and analysis, intermittent and dispersed work nodes can be identified on a timed or triggered basis. The specific monitoring method of the continuous video stream monitoring mode is as follows: continuous frame analysis technology is used to identify the action sequence of the operator in order to determine whether the operation sequence conforms to the standard.
6. The method according to claim 1, characterized in that, The real-time feedback and linkage response module is specifically used to: automatically trigger at least one of the following alarm methods according to the type and level of the anomaly: APP push, SMS notification, on-site audible and visual alarm, and attach screenshots or GIFs of abnormal evidence; The abnormality levels are classified into general violations and major hidden dangers based on the type and frequency of the abnormality. Major hidden dangers trigger on-site audible and visual alarms and simultaneously notify management personnel.
7. The method according to claim 1, characterized in that, The system also includes a statistical analysis module for: Generate full-process compliance rate reports, anomaly distribution heatmaps, and historical traceability reports, and export and visualize the data in multiple dimensions; Generate reports on the compliance rate and anomaly distribution of the entire job process, and filter and export data by time, job type, and node type.
8. The method according to claim 1, characterized in that, The system also includes a job scenario configuration module, which is used to predefine standard job process templates according to different job types. Users can adjust process nodes and configure monitoring strategies based on the predefined standard job process templates. The operation scenario configuration module supports multiple operation types, including building construction, industrial assembly, and equipment maintenance, and provides preset process nodes and tag libraries for each type.
9. A method for detecting standardized construction operation processes based on an algorithm model, characterized in that, include: S1. The workflow is divided into multiple independently configurable workflow nodes, and at least one monitoring tag and monitoring strategy are bound to each node; S2. Through the visual process orchestration interface, process nodes can be sorted, associated and configured by dragging and dropping to generate a process logic diagram; S3. Based on the dynamic monitoring tag library, the tag library is dynamically expanded and updated through a small sample training mechanism; S4. Based on the monitoring strategy configured for the node, use the multi-strategy AI monitoring engine to perform AI analysis on the real-time video stream or captured images to determine whether the node operation conforms to the preset specifications. S5. Summarize the detection results of each node, output an anomaly report, and trigger multi-level alarms and closed-loop processing instructions according to the anomaly level.
10. A storage medium, characterized in that, Used to store computer-executable instructions, which, when executed, implement the steps of the algorithm-based standardized process detection method for construction operations as described in claim 9.