Construction standard data management system based on constructional engineering

Abstract

The invention discloses a construction standard data management system based on building engineering, and aims to improve the building construction safety management efficiency and precision. The system comprises core components such as an inspection robot, a data processing module and a storage module. The inspection robot performs mobile monitoring in a building site according to a preset route, and acquires data such as images and videos in real time. The data processing module receives and processes the data, creatively and accurately measures and calculates the distance between the personnel and the equipment only by taking the point-to-point position visual data, and can classify and judge abnormal data according to a set rule, divide safety risk levels and realize intelligent identification and early warning of risks. And the storage module is responsible for storing and calling various data. According to the system, through flexible mobile monitoring of the inspection robot and in combination with an innovative data processing mode, a traditional building construction safety management means is innovated, safety accidents are effectively prevented, and smooth construction is guaranteed.

Description

Technical Field

[0001] This invention relates to the technical field of building engineering, and more particularly to a construction standard data management system based on building engineering. Background Technology

[0002] In traditional construction project management, safety monitoring at construction sites relies heavily on regular manual inspections and fixed monitoring equipment. Manual inspections suffer from low efficiency, susceptibility to subjective human factors, and an inability to provide real-time, comprehensive coverage. Fixed monitoring equipment has a limited monitoring range, struggles to adapt to the complex and ever-changing construction site environment, and often results in blind spots. For monitoring the safe distance between personnel and equipment, traditional methods require complex calculations and large amounts of data, consuming significant computational resources and lacking accuracy and timeliness. Furthermore, the efficiency of identifying and handling abnormal situations at construction sites is low, often only discovering problems after an accident has occurred. The lack of effective risk pre-assessment and classification early warning mechanisms fails to provide strong and timely decision support for construction safety management, making it difficult to meet the high standards of construction safety required in modern construction projects. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides a construction standard data management system based on building engineering. Through the mobile monitoring of inspection robots and the efficient processing of visual data, it enables rapid and accurate measurement of the distance between people and equipment at the construction site and automatic assessment of safety risks, providing strong technical support and assurance for the safety management of building construction.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is: to provide a construction standard data management system based on building engineering, comprising: The inspection robot moves along a preset path within the construction site, collecting images and video data of the construction site in real time. The data processing module is connected to the inspection robot to process the collected image and video data to determine the distance between the workers and the equipment, the workers' working posture and the wearing of safety gear, and thereby judge the safety risk level of the construction site. The storage module is used to store the data collected by the inspection robot and the results processed by the data processing module; A cloud server is connected to the storage module. The cloud server stores first construction standard data, which includes safety distance thresholds between workers and various equipment, standard postures for each construction process, and standards for safety gear required for each construction process. Managers adjust and update the first construction standard data in real time based on actual construction standards.

[0005] Preferably, the inspection robot also includes: The positioning device is used to determine the location information of the inspection robot in the construction site in real time, and to record the accurate location corresponding to the data when collecting data. At the same time, it assists the data processing module in analyzing the layout of the construction site and the distribution of personnel and equipment. The communication module employs wireless communication technology to ensure stable communication between the inspection robot and the data processing and storage modules, enabling real-time data transmission and interaction.

[0006] Preferably, the storage module also stores second construction standard data. When the data processing module analyzes and evaluates the construction site, it retrieves the construction standard data from the storage module, compares and verifies the implemented construction data with the construction standard data, and provides a reference for the classification of safety risk levels by mining and analyzing historical construction data.

[0007] More preferably, the second construction standard data in the storage module is synchronously updated at the start of the next work cycle after the first construction standard data on the cloud server has been adjusted and updated.

[0008] Preferably, the inspection robot also includes an alarm module, which is equipped with LED lights and a buzzer. The alarm module is connected to the data processing module. When the data processing module determines that there is a medium or high risk at the construction site, the alarm module issues a corresponding alarm signal to remind on-site construction personnel and management personnel to take timely measures to deal with the situation. Specifically, when the construction site is determined to be low-risk, the LED lights and buzzer will not activate; when the construction site is determined to be medium-risk, the LED lights will flash yellow and the buzzer will sound a warning; when the construction site is determined to be high-risk, the LED lights will flash red and the buzzer will sound a warning.

[0009] Preferably, the system also includes a display module, which is used to intuitively display the image and video data collected by the inspection robot, the distance measurement results between the staff and the equipment, the safety risk level assessment results, and the abnormal situation classification information of the construction site, so as to facilitate the management personnel to conduct real-time monitoring and management decisions of the construction site.

[0010] Preferably, the data processing module constructs a construction site safety assessment model based on the collected image and video data to obtain the distance between on-site workers and equipment, the workers' working posture, and the wearing of safety gear. This model is used to assess the safety risk score of the construction site in real time, and the safety risk score is used to classify the safety risk level.

[0011] More preferably, the construction site safety assessment model is defined as follows: ; Where D represents the safety risk score of the construction site, and α, β, γ, and θ are weighting coefficients used to control the impact of different violations on the overall model, and α + β + γ + θ = 1. This indicates the distance between worker i and the equipment. This represents the standard distance between worker i and the equipment. This represents the distance adjustment factor. This indicates the standard of worker i's work posture. The hazard score for not wearing safety gear according to standards (m) is obtained by looking up a table, where M represents the set of cases where safety gear was not worn according to standards (m). This indicates the hazard rating of the environment and equipment.

[0012] Preferably, the data processing module calculates the distance between workers and equipment using visual data from two points, and the data processing module is able to classify and judge abnormal situations at the construction site and classify safety risk levels.

[0013] More preferably, the criteria for classifying the security risk levels are as follows: Low risk: When the distance between workers and equipment is greater than or equal to the preset safe distance threshold, and there are no abnormal situations such as personnel violations or equipment malfunctions at the construction site, it is judged as low risk; Medium risk: When the distance between workers and equipment is less than the preset safe distance threshold, or when any of the following abnormal situations occur at the construction site: workers violating regulations, equipment failure, or improper material stacking, it is judged as medium risk. High risk: When the distance between people and equipment is less than the preset safe distance threshold, and any abnormal situation occurs at the construction site, such as workers violating regulations or major equipment failure, it is judged as high risk.

[0014] The beneficial effects of this invention are as follows: 1. Highly efficient and accurate monitoring: The inspection robot is flexible and can cover the construction site in all directions according to the preset route, eliminating blind spots in monitoring. Its method of calculating the distance between personnel and equipment by taking visual data from only two points simplifies the calculation process, greatly improves monitoring efficiency and accuracy, and can quickly identify potential safety risks between personnel and equipment, providing real-time and reliable data support for on-site safety management.

[0015] 2. Intelligent Risk Warning: The data processing module can perform in-depth analysis of various types of collected data, keenly identify abnormal data based on preset rules, accurately classify and judge the type of abnormality, and quickly classify the risk level according to the safety risk level classification standard, so as to realize intelligent and automated risk warning, enabling construction management personnel to intervene in advance and handle the situation in a targeted manner, effectively preventing the occurrence of safety accidents and nipping safety hazards in the bud.

[0016] 3. Enhance management efficiency: The system integrates data collection, processing, storage, and risk warning functions to create an integrated intelligent platform for construction management. This breaks through the traditional dilemma of lagging and scattered management information, enabling managers to have real-time control over the overall situation of the construction site, optimize management decisions based on the accurate risk information provided by the system, rationally allocate resources, and strengthen targeted safety training and on-site control, thereby comprehensively improving the level and efficiency of construction project management.

[0017] 4. Reduce accident losses: Through accurate risk warnings and efficient anomaly handling mechanisms, various safety threats at the construction site can be detected and mitigated in a timely manner, significantly reducing the probability of safety accidents. In the event of an anomaly, the system can respond quickly, guiding personnel evacuation and organizing rescue efforts to minimize casualties and property losses, ensuring the smooth progress of construction projects and creating good safety benefits.

[0018] 5. Facilitating Standardization: The system sets anomaly detection rules and risk classification standards based on relevant building construction standards and specifications, ensuring that on-site safety management strictly follows the requirements of the specifications, promoting the standardization and normalization of the construction process, laying a solid foundation for the high-quality development of the construction industry, and enhancing the competitiveness and reputation of construction companies in the market. Attached Figure Description

[0019] Figure 1 This is a framework diagram of a construction standard data management system based on building engineering, according to the present invention.

[0020] Figure 2 This is a schematic diagram of the structural framework of an inspection robot according to a preferred embodiment. Detailed Implementation

[0021] Please see Figure 1-2 As shown, this invention provides a construction standard data management system based on building engineering, comprising: The inspection robot moves along a preset path within the construction site, collecting images and video data of the construction site in real time. Inspection robot deployment: Place the inspection robot at the designated starting position on the construction site, ensure that it has sufficient power, and establish a stable communication connection with other modules of the system, such as the data processing module and storage module, via Wi-Fi, Bluetooth or 4G / 5G network.

[0022] The various sensors carried by the inspection robot, including distance sensors, image sensors, and gas sensors, are calibrated and their functions checked to ensure that the collected data is accurate and reliable.

[0023] Then check whether the hardware connections of the data processing module, storage module and other related equipment, including alarm devices, display terminals, etc., are normal to ensure that the entire system can work together.

[0024] The system's management software allows users to set preset inspection routes and key monitoring areas for the inspection robot based on the layout of the construction site, using a visual interface or coordinate input. Parameters such as the order, start point, and end point of the inspection route can be flexibly adjusted.

[0025] The system's main control interface initiates the entire construction standard data management system based on building engineering projects. The system automatically performs self-checks to verify the operational status of each module. If any anomalies are detected, the system will promptly prompt the user to troubleshoot and repair the problem.

[0026] The inspection robot begins its work: After the system is started, the inspection robot begins to move within the construction site according to the preset inspection route and time intervals. During the movement, it collects images, videos, and various sensor data (such as distance data, gas concentration data, etc.) of the construction site in real time, and transmits these data to the data processing module through the communication module.

[0027] Image and video acquisition: The inspection robot uses its onboard camera to capture images and videos of the construction site at a set frame rate, comprehensively recording the behavior of personnel, the operating status of equipment, and the stacking of materials. For example, it can capture whether personnel are wearing safety helmets, whether equipment is damaged, and whether materials are stacked in a standardized manner.

[0028] Sensor data acquisition: Distance sensors measure the distance between the inspection robot and surrounding objects (such as personnel, equipment, and material piles) in real time, providing data support for calculating the distance between personnel and equipment; gas sensors detect the air quality at the construction site, such as dust concentration and concentration of toxic and harmful gases; other sensors (such as temperature sensors and vibration sensors) collect physical parameters related to equipment operation.

[0029] The data processing module is connected to the inspection robot to process the collected image and video data to determine the distance between the workers and the equipment, the workers' working posture and the wearing of safety gear, and thereby judge the safety risk level of the construction site. In the data processing module, anomaly detection rules and safety risk level classification standards are set based on construction safety regulations, equipment operation standards, and internal management requirements. For example, safety distance thresholds between personnel and different types of equipment, normal ranges of equipment operating parameters, and boundary ranges of hazardous areas are defined.

[0030] Data Preprocessing: After receiving the raw data transmitted from the inspection robot, the data processing module first performs preprocessing operations. Image and video data undergo denoising, enhancement, and frame sampling to improve data quality and processing efficiency; sensor data undergoes format conversion, unit standardization, and filtering to ensure data consistency and accuracy.

[0031] Personnel and equipment distance calculation: Extract feature points of personnel and equipment from preprocessed image and video data, select visual data of two key points (such as the position of the person's head or shoulder and the edge or operating part of the equipment), and use geometric algorithms to calculate the actual distance between personnel and equipment.

[0032] Anomaly Detection and Classification: Based on preset anomaly detection rules, various types of collected data are analyzed and judged. Image recognition algorithms (such as convolutional neural networks) are used to identify whether personnel are operating in violation of regulations (such as not wearing safety equipment, entering dangerous areas, etc.) and whether equipment appearance is abnormal (such as cracks, deformation, etc.). Sensor data is compared and analyzed to determine whether equipment operating parameters exceed normal ranges and whether air quality exceeds standards. Detected abnormal data are classified according to predefined anomaly types, such as abnormal personnel behavior, abnormal equipment status, abnormal material management, and abnormal environmental conditions.

[0033] The data processing module uses the collected image and video data to obtain the distance between on-site workers and equipment, the workers' working posture, and the wearing of safety gear to construct a construction site safety assessment model, which is used to assess the safety risk score of the construction site in real time. The safety risk score is used to classify the safety risk level.

[0034] When calculating the distance between workers and equipment, the data processing module uses visual data from two points. Furthermore, the data processing module can classify and judge abnormal situations at the construction site and determine the level of safety risk.

[0035] Based on the safety risk level classification standards, and considering the type and severity of abnormal data, the safety risks at the construction site are assessed and classified in real time. The classification standards for safety risk levels are as follows: Low risk: When the distance between workers and equipment is greater than or equal to the preset safe distance threshold, and there are no abnormal situations such as personnel violations or equipment malfunctions at the construction site, it is judged as low risk; Medium risk: When the distance between workers and equipment is less than the preset safe distance threshold, or when any of the following abnormal situations occur at the construction site: workers violating regulations, equipment failure, or improper material stacking, it is judged as medium risk. High risk: When the distance between people and equipment is less than the preset safe distance threshold, and any abnormal situation occurs at the construction site, such as workers violating regulations or major equipment failure, it is judged as high risk.

[0036] Specifically, the determination of whether unauthorized construction behavior has occurred is based on the second standard data stored in the storage module, with the following settings: Safety-based rule setting: Safety regulations, industry standards, and internal safety management rules related to construction are compiled and transformed into a series of specific and operable detection rules. For example, if it is stipulated that personnel on construction sites must wear safety helmets, the detection rule would be: "In images collected by the inspection robot, the image recognition algorithm detects whether the personnel's head shows the characteristics of a safety helmet. If not, it is determined that the personnel are not wearing a safety helmet, which is an abnormal situation." Another example is the regulation that a pre-defined [X] meter radius around the operating area of ​​large equipment is a danger zone, prohibiting unauthorized personnel from entering. The detection rule would be: "Using images collected by the inspection robot and distance sensor data, the relative position of the personnel to the equipment operating area is identified. When personnel enter within [X] meters of the equipment operating area, it is determined that the personnel have entered the danger zone, which is abnormal."

[0037] Rule settings based on equipment operation standards: For the normal operating parameter ranges of various equipment on the construction site (such as temperature, pressure, vibration frequency, current, and voltage values), corresponding anomaly detection rules are set according to the technical manuals provided by the equipment manufacturers and industry-standard equipment operation standards. For example, the normal operating temperature range of a certain model of tower crane motor is [T1, T2]. If the inspection robot collects temperature data of the tower crane motor exceeding this range, it is determined that the equipment operating parameters are abnormal. Similarly, the normal vibration frequency of a concrete mixer should be within the range of [f1, f2]. Once the collected data exceeds this range, the equipment vibration anomaly detection rule is triggered.

[0038] Rule settings based on construction process specifications: Rules are established to check whether construction operations comply with standards and requirements for building construction. For example, in the scaffolding erection process, it is stipulated that each layer of scaffold boards must be laid tightly and tied securely. The detection rule is set as follows: "Using a high-definition camera mounted on an inspection robot to capture images of the scaffolding erection area, and using image analysis algorithms to detect the laying density and tying of the scaffold boards, if excessive gaps are found between the scaffold boards or the tying is not secure, it is judged as an abnormal scaffolding erection." In masonry construction, it is stipulated that the thickness of the mortar joints in the masonry blocks should be controlled within the range of [h1, h2]. The mortar joint thickness data is measured using an inspection robot and compared with the standard range; if it exceeds the range, it is judged as an abnormal mortar joint thickness in the masonry construction.

[0039] Rule settings based on site environmental requirements: Considering the impact of construction site environmental conditions on construction safety and quality, corresponding environmental anomaly detection rules are set. For example, it is stipulated that the material storage area of ​​the construction site should maintain a flat ground and unobstructed drainage. The detection rule is: "The inspection robot uses image acquisition and analysis to detect the flatness of the ground in the material storage area, and at the same time monitors whether the surrounding drainage facilities are unobstructed. If uneven ground or drainage blockage is found, it is judged as an environmental anomaly in the material storage area." As another example, regarding the air quality requirements of the construction site, when the dust monitoring sensor data shows that the dust concentration exceeds the dust emission standard limit for construction sites stipulated by the local environmental protection department, it is judged as an air quality anomaly.

[0040] Rule settings based on human construction posture: Human posture estimation technology is used to identify the position and angle of key body parts of construction workers (such as head, shoulders, waist, knees, etc.). Standard construction postures are defined according to construction standards and specifications. For example, when tying rebar, construction workers are required to maintain correct bending angles and arm postures. If the deviation of a construction worker's posture from the standard posture exceeds a set threshold, it is judged as an abnormal construction posture. Specifically, by analyzing the coordinates and angles of key points on the human body in the image, the construction worker's bending angle, arm raising angle, and other posture parameters are calculated and compared with standard parameters. If the deviation is too large, such as a bending angle exceeding 45 degrees and lasting for more than 10 seconds, an abnormal construction posture alarm is triggered.

[0041] The construction site safety assessment model is defined as follows: ; Where D represents the safety risk score of the construction site, and α, β, γ, and θ are weighting coefficients used to control the impact weight of different construction site violations on the overall model, and α + β + γ + θ = 1. This indicates the distance between worker i and the equipment. This represents the standard distance between worker i and the equipment. This represents the distance adjustment factor. This indicates the standard of worker i's work posture. The hazard score for not wearing safety gear according to standards (m) is obtained by looking up a table, where M represents the set of cases where safety gear was not worn according to standards (m). This indicates the hazard rating of the environment and equipment.

[0042] in The hazard score will vary depending on the distance between the staff and the equipment. When the distance between the staff and the equipment is less than the standard distance, the score will be negative and the hazard score will increase as the distance decreases. When the distance is greater than the standard distance, the score will be positive and the change will be smaller.

[0043] In one embodiment, the weighting coefficients are set as α=0.3, β=0.2, γ=0.25, θ=0.25, and kd=-1.2. The distance d between the worker and the concrete pump is calculated from the data collected by the inspection robot. i =1.8m, standard distance d0=3m, Act i =0.6, indicating that seat belts were not worn according to standard. W m =0.7, Hazard score for environment and equipment D e =0.6. Substituting into the model, we calculate: D = 0.3 × (1.8 - 3) / -1.2 + 0.2 × 0.6 + 0.25 × 0.7 + 0.25 × 0.6 ≈ 0.6 + 0.12 + 0.175 + 0.15 ≈ 1.045. According to the risk level classification standard, D = 1.045 is classified as a medium risk level.

[0044] In another embodiment, the weighting coefficients are set as follows: α=0.3, β=0.2, γ=0.25, θ=0.25, kd=-1.2. Data collected by the inspection robot yields the following: distance d1=1.2m between worker 3 and the bridge erecting machine, standard distance d0=4m, Act1=0.4, indicating a failure to wear safety helmets and safety belts as required, Wm=0.8, and environmental and equipment hazard score De=0.7. Substituting these values ​​into the model, the calculation is: D=0.3×(1.2-4) / -1.2+0.2×0.4+0.25×0.8+0.25×0.7≈0.7+0.08+0.2+0.175≈1.083. According to the risk level classification standard, D=1.083 is classified as a high-risk level.

[0045] The data processing module can rapidly process large amounts of data collected by inspection robots, performing real-time anomaly detection and risk assessment to promptly identify safety hazards at construction sites, significantly improving the efficiency of construction safety management. For example, utilizing efficient image recognition algorithms and geometric calculation methods, it can quickly calculate the distance between personnel and equipment and detect anomalies. Through advanced image recognition technology and precise geometric algorithms, it accurately calculates the distance between personnel and equipment and identifies various anomalies, reducing false alarms and missed alarms, providing reliable data support for construction safety management. For instance, it can accurately identify abnormal behaviors such as whether personnel are wearing safety helmets or entering dangerous areas. It can comprehensively analyze various data at the construction site, including personnel behavior, equipment status, material stacking, and environmental conditions, achieving all-round safety monitoring and ensuring the overall safety of the construction site. For example, it can simultaneously monitor equipment operating parameters and personnel operating behaviors, promptly identifying the correlation between equipment malfunctions and personnel violations. It possesses intelligent learning and adaptive capabilities, continuously optimizing anomaly detection rules and risk assessment models based on actual conditions and historical data at the construction site, improving the system's intelligence and adaptability. For example, by continuously updating and improving image recognition models through machine learning algorithms, the accuracy of identifying abnormal situations can be enhanced. Multiple functions are integrated into one system, including data processing, anomaly detection, risk assessment, and alarm control, forming a complete construction safety management solution that facilitates unified management and decision-making by managers. For instance, the data processing module is tightly integrated with the alarm module, enabling immediate triggering of the alarm mechanism upon detection of an anomaly, achieving rapid response.

[0046] The storage module is used to store the data collected by the inspection robot and the results processed by the data processing module; Within the storage module, a corresponding database structure is established to store the raw data collected by the inspection robot, the analysis results generated by the data processing module, anomaly records, and relevant construction standards and specifications. The database is then initialized and configured, setting parameters such as data storage path and storage period.

[0047] A cloud server is connected to the storage module. The cloud server stores first construction standard data, which includes safety distance thresholds between workers and various equipment, standard postures for each construction process, and standards for safety gear required for each construction process. Managers adjust and update the first construction standard data in real time based on actual construction standards.

[0048] The second construction standard data in the storage module is synchronized with the first construction standard data on the cloud server after the data has been adjusted and updated, at the start of the next work cycle.

[0049] The system also includes a display module, which is used to intuitively display the image and video data collected by the inspection robot, the distance measurement results between the staff and the equipment, the safety risk level assessment results, and the abnormal situation classification information of the construction site, so as to facilitate the management personnel to monitor and make management decisions in real time.

[0050] The system's display terminals (such as monitoring computers, tablets, or mobile phones) display real-time images and videos collected by the inspection robot, distance measurements between personnel and equipment, equipment operating parameters, and environmental data from the construction site. Simultaneously, intuitive icons, colors, or text prompts display the safety risk level and classification information of various abnormal situations at the construction site, facilitating management personnel's understanding of the construction status at any time.

[0051] The application of this system has enabled intelligent safety monitoring and management of construction sites. It can promptly detect personnel violations and equipment malfunctions, effectively reducing the risk of safety accidents at construction sites. For example, during an inspection, the system detected a construction worker entering a dangerous area near a foundation pit without a safety helmet, immediately issuing a high-risk alarm. Site management personnel arrived promptly and stopped the worker's violation, preventing a potential accident. Simultaneously, the system's real-time monitoring of equipment operating parameters helped detect abnormal motor temperature in the concrete mixer, allowing for timely repairs and preventing construction delays caused by equipment failure. Furthermore, the system detected some construction workers not properly wearing safety belts while working at heights, issuing timely alarms and prompting corrective action, further ensuring construction safety.

[0052] When the data processing module determines that there is a medium-risk or high-risk anomaly at the construction site, the system immediately triggers the corresponding alarm mechanism. Different alarm levels are used depending on the risk level. For example, for low-risk anomalies, the LED lights and buzzers are inactive; the system generates a reminder notification and sends it to on-site construction personnel and relevant management personnel, urging them to correct the situation promptly. For medium-risk anomalies, the LED lights flash yellow and the buzzer sounds a warning; while reminding relevant personnel, the data processing module further tracks and monitors this type of anomaly, analyzing its development trend. For high-risk anomalies, the LED lights flash yellow and the buzzer sounds a warning, and an SMS or system pop-up alarm notification is sent to the remote monitoring center. Simultaneously, an inspection robot is controlled to move to the vicinity of the anomaly location to conduct more detailed image acquisition and data monitoring, providing on-site personnel and management personnel with real-time information on the anomaly so they can take timely and effective emergency response measures, such as organizing personnel evacuation, stopping equipment operation, and conducting on-site rescue operations.

[0053] The system automatically records all detected anomalies, alarm information, and corresponding handling measures. Based on the recorded data, it generates anomaly statistical reports and safety risk analysis reports periodically or as needed. The reports include information such as the frequency of anomalies, their distribution areas, main anomaly types, and risk level distribution, providing managers with a detailed analysis of the construction site's safety management situation and assisting them in developing targeted safety management strategies and improvement measures.

[0054] Once the inspection robot completes the preset inspection route or reaches the set inspection time, it automatically returns to the designated charging location or standby area. During the return process, it continues to collect a small amount of data along the way to ensure the integrity and continuity of the inspection task.

[0055] Data storage and backup: The data processing module stores the processed results (including personnel and equipment distance measurement results, anomaly classification information, risk level assessment results, etc.) and the original collected data in the database of the storage module according to the set data storage structure and format. Simultaneously, the database is backed up regularly to prevent data loss or corruption, ensuring the integrity and security of the construction data.

[0056] Through the detailed operating steps described above, construction management personnel can make full use of this system to achieve intelligent and efficient safety monitoring and management of the construction site, promptly identify and address various safety hazards, and ensure the smooth progress of the construction project and the safety of life and property of construction workers.

[0057] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A construction standard data management system based on building engineering, characterized in that, include: The inspection robot moves along a preset path within the construction site, collecting images and video data of the construction site in real time. The data processing module is connected to the inspection robot to process the collected image and video data to determine the distance between the workers and the equipment, the workers' working posture and the wearing of safety gear, and thereby judge the safety risk level of the construction site. The storage module is used to store the data collected by the inspection robot and the results processed by the data processing module; A cloud server is connected to the storage module. The cloud server stores first construction standard data, which includes safety distance thresholds between workers and various equipment, standard postures for each construction process, and standards for safety gear required for each construction process. Managers adjust and update the first construction standard data in real time based on actual construction standards.

2. The construction standard data management system based on building engineering as described in claim 1, characterized in that, The inspection robot also includes: The positioning device is used to determine the location information of the inspection robot in the construction site in real time, and to record the accurate location corresponding to the data when collecting data. At the same time, it assists the data processing module in analyzing the layout of the construction site and the distribution of personnel and equipment. The communication module employs wireless communication technology to ensure stable communication between the inspection robot and the data processing and storage modules, enabling real-time data transmission and interaction.

3. The construction standard data management system based on building engineering as described in claim 1, characterized in that, The storage module also stores second construction standard data. When the data processing module analyzes and evaluates the construction site, it retrieves the construction standard data from the storage module, compares and verifies the implemented construction data with the construction standard data, and provides a reference for classifying safety risk levels by mining and analyzing historical construction data.

4. The construction standard data management system based on building engineering according to claim 3, characterized in that, The second construction standard data in the storage module is synchronized with the first construction standard data on the cloud server at the start of the next work cycle after the first construction standard data on the cloud server has been adjusted and updated.

5. A construction standard data management system based on building engineering as described in claim 1, characterized in that, The inspection robot also includes an alarm module, which is equipped with LED lights and a buzzer. The alarm module is connected to the data processing module. When the data processing module determines that there is a medium or high risk at the construction site, the alarm module issues a corresponding alarm signal to remind on-site construction personnel and management personnel to take timely measures to deal with the situation. Specifically, when the construction site is determined to be low-risk, the LED lights and buzzer will not activate; when the construction site is determined to be medium-risk, the LED lights will flash yellow and the buzzer will sound a warning; when the construction site is determined to be high-risk, the LED lights will flash red and the buzzer will sound a warning.

6. A construction standard data management system based on building engineering according to claim 1, characterized in that, The system also includes a display module, which is used to intuitively display the image and video data collected by the inspection robot, the distance measurement results between the staff and the equipment, the safety risk level assessment results, and the abnormal situation classification information of the construction site, so as to facilitate the management personnel to monitor and make management decisions in real time.

7. A construction standard data management system based on building engineering as described in claim 1, characterized in that, The data processing module uses the collected image and video data to obtain the distance between on-site workers and equipment, the workers' working posture, and the wearing of safety gear to construct a construction site safety assessment model, which is used to assess the safety risk score of the construction site in real time. The safety risk score is used to classify the safety risk level.

8. A construction standard data management system based on building engineering according to claim 7, characterized in that, The construction site safety assessment model is defined as follows: ; Where D represents the safety risk score of the construction site, and α, β, γ, and θ are weighting coefficients used to control the impact weight of different construction site violations on the overall model, and α + β + γ + θ = 1. This indicates the distance between worker i and the equipment. This represents the standard distance between worker i and the equipment. This represents the distance adjustment factor. This indicates the standard of worker i's work posture. The hazard score for not wearing safety gear according to standards (m) is obtained by looking up a table, where M represents the set of cases where safety gear was not worn according to standards (m). This indicates the hazard rating of the environment and equipment.

9. A construction standard data management system based on building engineering according to claim 1, characterized in that, When calculating the distance between workers and equipment, the data processing module uses visual data from two points. Furthermore, the data processing module can classify and judge abnormal situations at the construction site and determine the level of safety risk.

10. A construction standard data management system based on building engineering according to claim 9, characterized in that, The criteria for classifying the security risk levels are as follows: Low risk: When the distance between workers and equipment is greater than or equal to the preset safe distance threshold, and there are no abnormal situations such as personnel violations or equipment malfunctions at the construction site, it is judged as low risk; Medium risk: When the distance between workers and equipment is less than the preset safe distance threshold, or when any of the following abnormal situations occur at the construction site: workers violating regulations, equipment failure, or improper material stacking, it is judged as medium risk. High risk: When the distance between people and equipment is less than the preset safe distance threshold, and any abnormal situation occurs at the construction site, such as workers violating regulations or major equipment failure, it is judged as high risk.

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