Intelligent construction site intelligent monitoring system and equipment based on AR and multi-source data fusion

Abstract

The invention relates to the technical field of intelligent construction site management, and particularly discloses an intelligent monitoring system and equipment for an intelligent construction site based on AR and multi-source data fusion, and the system can monitor the construction site more comprehensively and more accurately through fusion of the AR technology and multi-source data, and deal with sudden risks more quickly and effectively. The core of the method is that monitoring vision field shielding parameters and monitoring accuracy indexes are utilized, monitoring vision field shielding is analyzed, compensation adjustment is carried out, the monitoring coverage range and effect are remarkably improved, the accuracy degree of shielding compensation is evaluated in real time through a unique judgment optimization module, self-adaptive updating and optimization are carried out on a compensation strategy accordingly, and the monitoring accuracy is improved. And in combination with a space optimization risk early warning mechanism of an intelligent construction site attribute parameter, an abnormal region reference library is dynamically matched, and accurate positioning of an abnormal region is realized. According to the invention, powerful support is provided for constructing a safer and more intelligent modernized construction site.

Description

Technical Field

[0001] This invention relates to the field of smart construction site management technology, specifically to a smart construction site intelligent monitoring system and equipment based on AR and multi-source data fusion. Background Technology

[0002] In current smart construction site monitoring, high-definition cameras and sensors are widely deployed on construction sites to collect images and environmental data from multiple angles. When obstruction occurs, some systems first utilize image information from adjacent unobstructed cameras, employing image stitching and fusion technology to attempt to reconstruct the scene of the obstructed area. Simultaneously, sensors continuously collect data such as personnel locations and equipment operating status, cross-validating this information with the image data. Backend intelligent analysis algorithms combine this multi-source data, using deep learning models to predict and complete the occlusion situation, presenting the real-time status of the construction site as accurately as possible. This provides managers with comprehensive and reliable monitoring information, assisting them in making informed decisions.

[0003] For example, the Chinese invention patent with announcement number CN117035456B discloses a smart construction site monitoring and management method and system, which involves the field of construction site management technology, including: data collection, data processing, data anomaly detection, processing suggestion recommendation, monitoring result log generation, monitoring result sending, etc. By combining reinforcement learning and knowledge base to perform multiple rounds of iterative anomaly detection on monitoring data, abnormal data and anomaly degree are obtained, and processing suggestions are obtained according to the set recommendation process.

[0004] For example, Chinese invention patent CN116245418B discloses a data mining-based method for smart construction site safety risk assessment, which relates to the field of data mining technology. This invention includes the following steps: 1. Obtaining various safety-related datasets from real construction sites; 2. Constructing a construction site safety evaluation system based on a thorough study of the construction site safety risk assessment process and elements; 3. Performing data analysis based on the datasets of various indicators in the constructed construction site safety evaluation system; 4. Employing a weighted allocation and matrix analysis-based approach. 5. The improved AHP method and data-based entropy weight method are used to calculate the subjective and objective weights of each indicator in the indicator layer of the evaluation system; The evidence fusion algorithm synthesizes multi-source evidence and obtains indicator weights; 6. Calculate the comprehensive evaluation index of the construction site.

[0005] However, in the process of implementing the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: When dealing with the occlusion problem, the existing smart construction site monitoring system usually puts it directly into subsequent use after completing the occlusion compensation. However, the occlusion compensation operation will cause a change in the state of the equipment, which may cause the equipment to be unable to work in the best state, thereby affecting the quality of the monitoring data and the system's accurate monitoring of the construction site conditions. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a smart construction site monitoring system and equipment based on AR and multi-source data fusion, which can effectively solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a smart construction site intelligent monitoring system based on AR and multi-source data fusion, comprising: an analysis and judgment module, used to utilize AR technology and fuse multi-source data to construct a smart construction site, collect and analyze the occlusion parameters of the monitoring field of view of the smart construction site, and determine whether to adjust the AR monitoring of the smart construction site for occlusion compensation; a judgment and optimization module, used to collect and analyze the monitoring accuracy indicators of the smart construction site, and determine whether the attribute parameters of the smart construction site can be collected; if the attribute parameters of the smart construction site cannot be collected, the AR monitoring of the smart construction site is optimized; if the attribute parameters of the smart construction site can be collected, the attribute parameters of the construction site are analyzed; and an adjustment and early warning module, used to obtain the project progress of the smart construction site and locate abnormal areas of the smart construction site by analyzing the attribute parameters of the smart construction site, and thus provide early warning prompts.

[0008] As a further solution, a determination is made regarding whether to apply occlusion compensation adjustment to the AR monitoring of the smart construction site. The specific determination process is as follows: The occlusion parameters of the monitoring field of view of the smart construction site are analyzed to obtain the occlusion index of the monitoring field of view of the smart construction site, and compared with the occlusion threshold of the monitoring field of view stored in the database. If the occlusion index of the monitoring field of view of the smart construction site is less than or equal to the occlusion threshold of the monitoring field of view, it is determined that no occlusion compensation adjustment will be applied to the AR monitoring of the smart construction site. If the occlusion index of the monitoring field of view of the smart construction site is greater than the occlusion threshold of the monitoring field of view, it is determined that occlusion compensation adjustment will be applied to the AR monitoring of the smart construction site, and the millimeter-wave radar array will be activated at the reference scanning frequency. The occlusion index of the monitoring field of view of the smart construction site is used to characterize the degree of monitoring occlusion within the monitoring field of view of the smart construction site.

[0009] As a further solution, occlusion compensation adjustment is performed on AR monitoring. The specific adjustment process is as follows: Based on the detection results of the millimeter-wave radar array, the occlusion type of the smart construction site is determined. If it is determined to be diffuse occlusion, an environmental risk warning is directly issued. If it is determined to be structural occlusion, a scanning frequency amplification coefficient is matched from the monitoring database based on the occlusion index and occlusion threshold of the monitoring field of the smart construction site. The scanning frequency of the millimeter-wave radar array of the smart construction site is increased and adjusted using the scanning frequency amplification coefficient. After the amplification adjustment, the occlusion index of the monitoring field of view after the amplification adjustment is obtained again and compared with the occlusion threshold of the monitoring field of view to determine whether a secondary occlusion compensation adjustment is needed for AR monitoring.

[0010] As a further solution, the AR monitoring of the smart construction site is optimized. The specific optimization process is as follows: Based on the monitoring accuracy index and monitoring accuracy threshold of the smart construction site, the maximum scanning frequency of the millimeter-wave radar is determined by matching the monitoring database; the scanning frequency of the millimeter-wave radar is adjusted by defining the maximum scanning frequency to achieve one adjustment; the monitoring accuracy index of the smart construction site after the adjustment is obtained again and compared with the monitoring accuracy threshold. If the monitoring accuracy index of the smart construction site after the adjustment is greater than or equal to the monitoring accuracy threshold, the millimeter-wave radar continues to be transmitted at the current scanning frequency; if the monitoring accuracy index of the smart construction site after the adjustment is less than the monitoring accuracy threshold, the deviation value of the monitoring accuracy index of the smart construction site after the adjustment is obtained and compared with the monitoring accuracy threshold. The system compares the monitoring accuracy deviation value with a preset threshold value in the database. If the monitoring accuracy index deviation value of the smart construction site after the first adjustment is less than or equal to the monitoring accuracy deviation value threshold, the system continues to transmit millimeter-wave radar at the current scanning frequency. If the monitoring accuracy index deviation value of the smart construction site after the first adjustment is greater than the monitoring accuracy deviation value threshold, the system matches the maximum power of the millimeter-wave radar from the monitoring database. The system adjusts the millimeter-wave radar power by defining the maximum power to achieve a second adjustment. The system then obtains the monitoring accuracy index of the smart construction site after the second adjustment and compares it with the monitoring accuracy threshold. If the monitoring accuracy index of the smart construction site after the second adjustment is greater than or equal to the monitoring accuracy threshold, the system continues to transmit millimeter-wave radar at the current scanning frequency and power.

[0011] If the monitoring accuracy index of the smart construction site after the second adjustment is still less than the monitoring accuracy threshold, a level-two monitoring risk warning will be issued immediately.

[0012] The aforementioned monitoring accuracy threshold refers to the preset monitoring accuracy index in the monitoring database, which is the minimum permissible value of the preset monitoring accuracy index in the monitoring database.

[0013] The deviation value of the monitoring accuracy index of the smart construction site after the above adjustment refers to the preset defined monitoring accuracy index in the monitoring database minus the monitoring accuracy index of the smart construction site after the first adjustment.

[0014] The aforementioned monitoring accuracy deviation threshold refers to the preset monitoring accuracy index deviation value in the monitoring database, which is the maximum permissible value for the preset monitoring accuracy deviation value in the monitoring database.

[0015] The second aspect of this invention provides a smart construction site monitoring device based on AR and multi-source data fusion, including: AR glasses and a multimodal sensor; the AR glasses refer to a dynamic occlusion compensation device for smart construction sites, used to compensate for monitoring occlusion in smart construction sites and to collect and transmit occlusion parameters of the monitoring field of view to generate dynamic compensation images of the occluded area in real time through augmented reality technology; the multimodal sensor synchronously collects monitoring data and spatial coordinate information within the monitoring field of view.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) This invention provides a smart construction site intelligent monitoring system and equipment based on AR and multi-source data fusion. By using AR technology and fusing multi-source data, a smart construction site is constructed. The system collects and analyzes the occlusion parameters of the monitoring field of the smart construction site and adjusts the occlusion compensation for the AR monitoring of the smart construction site. Compared with traditional monitoring methods, this AR-based monitoring method can monitor the construction site more comprehensively and respond to sudden risks more quickly and effectively. Through the judgment optimization module, the system can dynamically optimize the monitoring process using AR technology according to the monitoring accuracy indicators, further improving the system's adaptability. This invention provides a more comprehensive and accurate intelligent monitoring and occlusion compensation solution for smart construction sites, and provides strong support for building a safer modern construction site.

[0017] (2) The present invention is based on the judgment and optimization module of the monitoring field occlusion index and the monitoring accuracy index. By collecting the monitoring accuracy index of the smart construction site in real time and combining the secondary coupling of the monitoring field occlusion, the system can quantitatively evaluate the accuracy of the monitoring occlusion compensation, automatically adjust the AR monitoring, and based on the evaluation results, the system realizes the adaptive update and optimization of the monitoring occlusion compensation, forming a "monitoring-evaluation-optimization" closed loop, realizing the dynamic control of the AR monitoring compensation strategy, forming a closed loop dynamic optimization and adaptive synergy of multi-source data contribution.

[0018] (3) This invention realizes intelligent management of “data collection - anomaly identification - precise positioning - linkage early warning” by using a spatial optimization risk early warning mechanism based on smart construction site attribute parameters. By integrating monitoring field obstruction parameters and monitoring accuracy indicators, it dynamically matches the abnormal area benchmark library, realizes the precise positioning of abnormal areas, optimizes the allocation of safety resources, and provides precise data support for decision-making, thereby comprehensively improving the construction site’s proactive safety protection capabilities and overall safety management level. Attached Figure Description

[0019] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0021] Figure 2 This is a flowchart of the smart construction site AR monitoring occlusion compensation adjustment determination process of the present invention.

[0022] Figure 3 This is a flowchart of the intelligent construction site occlusion type determination and processing method of the present invention.

[0023] Figure 4 This is a flowchart of the secondary occlusion compensation determination process for the smart construction site AR monitoring system of the present invention.

[0024] Figure 5 This is a schematic diagram illustrating the data-driven decision-making process for intelligent construction site compensation according to the present invention.

[0025] Figure 6 Optimize the closed-loop flowchart for determining the accuracy of smart construction site monitoring.

[0026] Figure 7 This is a flowchart illustrating the risk prevention and control process for smart construction site monitoring in this invention.

[0027] Figure 8 This is a flowchart of the smart construction site dynamic risk early warning decision-making process of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 As shown, the first aspect of the present invention provides a smart construction site intelligent monitoring system based on AR and multi-source data fusion, including: an analysis and judgment module, a judgment optimization module, an adjustment and early warning module, and a monitoring database.

[0030] The monitoring database stores parameters of a smart construction site monitoring system based on AR and multi-source data fusion.

[0031] The analysis and judgment module is connected to the adjustment and early warning module and the judgment and optimization module. The judgment and optimization module is connected to the adjustment and early warning module. The analysis and judgment module, the judgment and optimization module and the adjustment and early warning module are all connected to the monitoring database.

[0032] The analysis and judgment module is used to utilize AR technology and fuse multi-source data to construct a smart construction site. It collects and analyzes the occlusion parameters of the monitoring field of view of the smart construction site to determine whether to adjust the AR monitoring of the smart construction site for occlusion compensation.

[0033] Reference Figure 2 As shown: First, occlusion parameters of the monitoring field of view are collected. Based on these parameters, the occlusion index of the monitoring field of view is calculated. If the occlusion index of the monitoring field of view is less than or equal to the preset occlusion threshold, it is determined that no occlusion compensation adjustment is needed for AR monitoring. If the occlusion index of the monitoring field of view exceeds the threshold, it is determined that occlusion compensation adjustment needs to be initiated, and at the same time, the millimeter-wave radar array is activated for auxiliary scanning at the reference scanning frequency. This process ensures that AR monitoring maintains an effective field of view in complex construction site environments by dynamically assessing the degree of occlusion.

[0034] Specifically, the process for determining whether to adjust the occlusion compensation for the AR monitoring of the smart construction site is as follows: Analyze the occlusion parameters of the monitoring field of view of the smart construction site to obtain the occlusion index, and compare it with the occlusion threshold stored in the database. If the occlusion index is less than or equal to the occlusion threshold, it is determined that no occlusion compensation adjustment will be made for the AR monitoring of the smart construction site. If the occlusion index is greater than the occlusion threshold, it is determined that occlusion compensation adjustment will be made for the AR monitoring of the smart construction site, and the millimeter-wave radar array will be activated at the reference scanning frequency.

[0035] The aforementioned AR monitoring refers to intelligent monitoring that uses augmented reality technology to monitor smart construction sites.

[0036] The aforementioned occlusion threshold for the monitoring field of view refers to the maximum permissible value of the occlusion index for the smart construction site monitoring field of view preset in the monitoring database.

[0037] The occlusion parameters of the monitoring field of view of a smart construction site include the dust extinction coefficient of the smart construction site, the dynamic occlusion area of ​​the smart construction site, and the camera field of view deflection of the smart construction site.

[0038] The aforementioned dust extinction coefficient refers to the coefficient that quantifies the ability of a unit volume of dust in a smart construction site to attenuate the light in the monitoring image; the aforementioned dynamic occlusion area refers to the union of the areas of the monitoring field of view of dynamic objects in the smart construction site that are occluded in the field of view of each camera; the aforementioned camera field of view deflection angle refers to the angle between the central optical axis of the camera in the smart construction site and the normal of the target construction site monitoring area; wherein the dust extinction coefficient can be obtained by real-time collection and monitoring of the concentration and transmittance of small particulate matter in the smart construction site through a laser scattering sensor and a wavelength transmission meter, and the camera field of view deflection angle can be obtained by the camera's built-in dual-axis tilt sensor and built-in laser ranging array.

[0039] It needs to be explained that the dust extinction coefficient is determined by deploying laser scattering sensors and wavelength transmissometers within the smart construction site. Through the collaborative operation of these sensors and transmissometers, suspended microparticles and wavelength light signals within the smart construction site are monitored in real time. This quantifies the attenuation effect of dust per unit volume on the light in the monitoring image. It refers to the overall attenuation intensity of dust within a unit volume of space on the wavelength light signal in the monitoring field. By introducing proportional coefficients to quantify the fixed optical path and light transmittance of the smart construction site, and then de-unitizing these parameters, the dust extinction coefficient of the smart construction site is obtained. The specific expression is as follows: ; In the formula, kd is the dust extinction coefficient of the smart construction site, gc is the fixed optical path of the smart construction site, tc is the light transmittance of the smart construction site, f1 is the proportional coefficient corresponding to the fixed optical path preset in the monitoring database, and f2 is the proportional coefficient corresponding to the light transmittance preset in the monitoring database.

[0040] The fixed optical path of the aforementioned smart construction site refers to the fixed light propagation distance between the transmitting and receiving ends inside the wavelength transmissometer; the light transmittance of the aforementioned smart construction site refers to the degree to which dust in the smart construction site transmits the monitoring light.

[0041] The proportional coefficient corresponding to the fixed optical path mentioned above represents the degree of influence of the fixed optical path unit value on the dust extinction coefficient, and is preset in the monitoring database; the proportional coefficient corresponding to the light transmittance mentioned above represents the degree of influence of the light transmittance on the dust extinction coefficient, and is preset in the monitoring database.

[0042] By introducing contribution coefficients, the proportions of the dust extinction coefficient to the defined dust extinction coefficient, the dynamic occlusion area to the monitoring field of view, and the camera's field of view deflection angle to the defined camera's field of view deflection angle are quantified to determine their respective contributions to the monitoring field of view occlusion index. These contributions are then aggregated to derive the monitoring field of view occlusion index for the smart construction site. The specific expression for the monitoring field of view occlusion index for the smart construction site is as follows: ; In the formula, SOI is the occlusion index of the monitoring field of view of the smart construction site, kd is the dust extinction coefficient of the smart construction site, A_dynamic is the dynamic occlusion area of ​​the smart construction site, cd is the camera field of view deflection angle of the smart construction site, kd_max is the preset defined dust extinction coefficient in the monitoring database, A_total is the monitoring field of view area of ​​the smart construction site, cd_max is the preset defined camera field of view deflection angle in the monitoring database, α is the contribution coefficient corresponding to the preset dust extinction coefficient in the monitoring database, β is the contribution coefficient corresponding to the preset dynamic occlusion area in the monitoring database, and γ is the contribution coefficient corresponding to the preset camera field of view deflection angle in the monitoring database.

[0043] The aforementioned smart construction site monitoring field of view occlusion index is a quantitative indicator composed of the degree of occlusion of the monitoring field of view of the smart construction site, used to characterize the degree of monitoring occlusion within the monitoring field of view of the smart construction site.

[0044] The dust extinction coefficient defined above represents the maximum permissible value of the dust extinction coefficient preset in the monitoring database; the monitoring field of view area defined above represents the projected area of ​​the smart construction site space effectively covered by the monitoring equipment; the camera field of view deflection defined above represents the maximum permissible value of the camera field of view deflection preset in the monitoring database.

[0045] The contribution coefficient corresponding to the dust extinction coefficient mentioned above represents the contribution of the ratio between the dust extinction coefficient of the smart construction site and the defined dust extinction coefficient to the occlusion index of the monitoring field of view. It is preset in the monitoring database and the value range is (0, 1]. The contribution coefficient corresponding to the dynamic occlusion area mentioned above represents the contribution of the ratio between the dynamic occlusion area of ​​the smart construction site and the defined monitoring field of view area of ​​the smart construction site to the occlusion index of the monitoring field of view. It is preset in the monitoring database and the value range is (0, 1). The contribution coefficient corresponding to the camera field of view deflection angle mentioned above represents the contribution of the ratio between the camera field of view deflection angle of the smart construction site and the defined camera field of view deflection angle to the occlusion index of the monitoring field of view. It is preset in the monitoring database and the value range is (0, 1).

[0046] It should be explained that an increase in the dust extinction coefficient means that the attenuation ability of a unit volume of dust in a smart construction site on monitoring light is enhanced, directly leading to a decrease in the clarity of the monitoring image. The ratio of the dust extinction coefficient to the defined dust extinction coefficient of the smart construction site increases, and with other parameters remaining unchanged, the occlusion index of the monitoring field of view increases. An increase in the dynamic occlusion area means that the physical obstruction of the monitoring field of view by structural occlusion is enhanced. The ratio of the dynamic occlusion area to the monitoring field of view area of ​​the smart construction site increases, and with other parameters remaining unchanged, the occlusion index of the monitoring field of view increases. An increase in the camera field of view deflection angle means that the effective monitoring range is reduced due to camera pose deviation. The ratio of the camera field of view deflection angle to the defined camera field of view deflection angle of the smart construction site increases, and with other parameters remaining unchanged, the occlusion index of the monitoring field of view increases. In summary, the dust extinction coefficient, dynamic occlusion area, and camera field of view deflection angle directly affect the occlusion index of the monitoring field of view, and all of them show a positive correlation with the occlusion index of the monitoring field of view.

[0047] Reference Figure 3 As shown: First, the occlusion type is identified. If it is diffuse occlusion, an environmental risk warning is triggered. If it is structural occlusion, the millimeter-wave radar scanning frequency amplification coefficient is dynamically matched and the first adjustment is implemented. Then, the occlusion index deviation value of the adjusted monitoring field of view is detected in real time. If the occlusion index deviation value of the adjusted monitoring field of view is less than or equal to the monitoring occlusion deviation value threshold, the optimization module is entered. Otherwise, the deviation value is recalculated and a second adjustment is initiated, forming an intelligent closed-loop control mechanism of "type recognition - parameter matching - effect verification" to ensure that AR monitoring maintains an effective field of view under complex working conditions.

[0048] Specifically, occlusion compensation adjustment is performed on AR monitoring. The adjustment process is as follows: Based on the detection results of the millimeter-wave radar array, the type of occlusion in the smart construction site is determined. If it is determined to be diffuse occlusion, an environmental risk warning is directly issued. If it is determined to be structural occlusion, a scanning frequency amplification coefficient is matched from the monitoring database based on the occlusion index and occlusion threshold of the smart construction site's monitoring field of view. The scanning frequency of the millimeter-wave radar array in the smart construction site is increased and adjusted using the scanning frequency amplification coefficient. The millimeter-wave radar array scans after the amplification adjustment to enhance the acquisition capability of the part of the field of view obstructed by structural occlusion, thereby reducing the degree of occlusion in the monitoring field of view. Based on the scanning results of the millimeter-wave radar array, occlusion compensation adjustment is performed on AR monitoring, updating the occlusion index of the smart construction site's monitoring field of view. The occlusion index of the smart construction site after the amplification adjustment is obtained is compared with the occlusion threshold of the monitoring field of view to determine whether a secondary occlusion compensation adjustment is needed for AR monitoring.

[0049] It should be explained that the scanning frequency amplification factor is multiplied by the scanning frequency of the millimeter-wave radar array of the smart construction site to increase the scanning frequency of the millimeter-wave radar array.

[0050] It should be explained that millimeter-wave radar detection arrays are multi-sensor collaborative detection systems based on high-frequency electromagnetic waves.

[0051] The aforementioned diffuse occlusion refers to the occlusion of the monitoring field of view caused by diffuse dust within the monitoring field of view of the smart construction site; the aforementioned structural occlusion refers to the occlusion of the monitoring field of view caused by structural objects within the monitoring field of view of the smart construction site.

[0052] The aforementioned environmental risk warning refers to the smart construction site monitoring system displaying a pop-up window to the administrator, indicating that the current construction site monitoring field of view has low visibility and there is an environmental risk.

[0053] It needs to be explained that, based on the occlusion index and occlusion threshold of the monitoring field of view of the smart construction site, the scanning frequency amplification coefficient is matched from the monitoring database, and the predefined scanning frequency mapping function in the monitoring database is called. This paper quantifies the mapping relationship between the occlusion index factor of the monitoring field of view and the scanning frequency of the millimeter-wave radar. The curvature of the power function is adjusted by a nonlinear adjustment exponent *n*, increasing the intensity of the change in the millimeter-wave radar scanning frequency with respect to the occlusion index factor. Here, *A* is the amplification coefficient of the millimeter-wave radar scanning frequency, *k* is the proportional coefficient corresponding to the preset occlusion index factor in the monitoring database, *SOI* is the occlusion index of the smart construction site's monitoring field, *SOI_MAX* is the preset occlusion threshold in the monitoring database, *n* is the nonlinear adjustment exponent corresponding to the preset occlusion index factor in the monitoring database, used to quantify the nonlinear characteristics of the mapping function, representing the non-fixed proportional mapping relationship between the millimeter-wave radar scanning frequency and the occlusion index factor, and *b* is the frequency lower limit compensation value defined in the monitoring database, input to the smart construction site's monitoring... The view occlusion index and the monitoring view occlusion threshold are mapped to a mapping function to calculate the scanning frequency amplification factor of the millimeter-wave radar. Besides the method described in the above embodiment, other similar mapping functions can also be used. The nonlinear adjustment index corresponding to the aforementioned monitoring view occlusion index factor represents the degree of nonlinear influence of the ratio between the monitoring view occlusion index and the monitoring view occlusion threshold of the smart construction site on the millimeter-wave radar scanning frequency. By inputting the keyword "millimeter-wave radar scanning frequency" into the monitoring database, a preset millimeter-wave radar scanning frequency nonlinear adjustment index mapping table is retrieved. Then, the monitoring view occlusion index and the monitoring view occlusion threshold of the smart construction site are input into the millimeter-wave radar scanning frequency nonlinear adjustment index mapping table to map the nonlinear adjustment index corresponding to the input monitoring view occlusion index of the smart construction site.

[0054] It should be explained that if the occlusion index of the monitoring field of view of the smart construction site is greater than the occlusion threshold, then it is determined that the occlusion compensation adjustment should be performed on the AR monitoring of the smart construction site. The occlusion compensation adjustment is performed on the AR monitoring based on the result of determining the occlusion type of the smart construction site. If the occlusion type is determined to be diffuse environmental occlusion, the occlusion index of the monitoring field of view with diffuse environmental occlusion type cannot be adjusted due to the non-adjustability of the environment, and an environmental risk warning is directly issued.

[0055] Reference Figure 4 As shown: First, the occlusion index of the monitoring field of view after the increase adjustment is obtained, and it is compared with the occlusion threshold of the monitoring field of view. If the occlusion index of the monitoring field of view after the increase adjustment is less than or equal to the threshold, it is determined that no secondary compensation adjustment will be performed. If the occlusion index of the monitoring field of view after the increase adjustment is greater than the threshold, the deviation value of the monitoring field of view occlusion index is calculated first, and then the preset monitoring occlusion deviation value threshold is obtained. Finally, the deviation value of the monitoring field of view occlusion index is compared with the preset threshold to determine whether further processing is required. This is used to determine whether secondary compensation adjustment should be carried out after the increase adjustment and the subsequent operation logic.

[0056] Furthermore, to determine whether to perform secondary occlusion compensation adjustment on AR monitoring, the specific determination process is as follows: compare the occlusion index of the monitoring field of the smart construction site after the adjustment with the occlusion threshold of the monitoring field. If the occlusion index of the monitoring field of the smart construction site after the adjustment is less than or equal to the occlusion threshold of the monitoring field, it is determined that no secondary occlusion compensation adjustment should be performed on AR monitoring.

[0057] If the occlusion index of the smart construction site's monitoring field of view is still greater than the occlusion threshold after the adjustment is increased, it is determined that a second occlusion compensation adjustment will be performed on the AR monitoring. The specific compensation adjustment process is as follows: obtain the deviation value of the occlusion index of the smart construction site's monitoring field of view after the adjustment is increased, and compare it with the preset monitoring occlusion deviation value threshold in the monitoring database. If the deviation value of the occlusion index of the smart construction site's monitoring field of view after the adjustment is increased is less than or equal to the monitoring occlusion deviation value threshold, then a second occlusion compensation adjustment will be performed on the smart construction site through the first compensation scheme.

[0058] If the deviation value of the occlusion index of the smart construction site's monitoring field of view after the adjustment is increased is greater than the monitoring occlusion deviation value threshold, then the smart construction site will be adjusted for secondary occlusion compensation through the second compensation scheme, and a first-level monitoring risk warning will be issued at the same time.

[0059] The above-mentioned increase in the occlusion index deviation value of the smart construction site's monitoring field of view after adjustment refers to the occlusion index of the smart construction site's monitoring field of view after adjustment minus the preset occlusion threshold of the monitoring field of view in the monitoring database.

[0060] The aforementioned increased and adjusted monitoring field occlusion index of the smart construction site refers to the monitoring field occlusion index of the smart construction site after the occlusion type is determined to be structural occlusion and the scanning frequency of the millimeter-wave radar array of the smart construction site is increased and adjusted.

[0061] The aforementioned monitoring occlusion deviation threshold is the maximum permissible value of the monitoring field occlusion index deviation preset in the monitoring database.

[0062] It should be explained that the Level 1 monitoring risk warning is a pop-up window from the smart construction site monitoring system that displays a risk to the administrator.

[0063] It needs to be explained that the AR monitoring undergoes secondary occlusion compensation adjustment. After adjusting the AR monitoring of the smart construction site based on the occlusion type result, the occlusion parameters of the monitoring field of view of the smart construction site are collected and analyzed again. The occlusion index of the monitoring field of view of the smart construction site after the adjustment is increased is obtained and compared with the occlusion threshold of the monitoring field of view. If the occlusion index of the monitoring field of view of the smart construction site after the adjustment is still greater than the occlusion threshold of the monitoring field of view, the deviation value of the occlusion index of the monitoring field of view of the smart construction site after the adjustment is obtained. The AR monitoring is then adjusted for secondary occlusion compensation based on the deviation value of the occlusion index of the monitoring field of view of the smart construction site after the adjustment and the occlusion deviation value threshold.

[0064] Reference Figure 5 As shown: Both the linked compensation scheme and the linear compensation scheme are generated based on the real-time comparison results of the occlusion index deviation value and the threshold of the monitoring field of view, and dynamically query the historical parameters in the monitoring database; the focal length calibration operation obtains the millimeter-wave radar detection results in real time and links the equipment calibration parameters in the monitoring database. This process achieves dynamic optimization of the compensation strategy and precise execution of equipment control through a dual-path data interaction architecture.

[0065] Specifically, the first compensation scheme is used to adjust the secondary occlusion compensation of the smart construction site. The specific adjustment process is as follows: The first compensation scheme refers to the linkage compensation scheme based on the increased and adjusted occlusion index deviation value of the smart construction site and the monitoring occlusion deviation value threshold. At the same time, the focal length calibration coefficient of the monitoring equipment is matched based on the millimeter-wave radar array detection data, and the focal length of the monitoring equipment is calibrated through the focal length calibration coefficient of the monitoring equipment.

[0066] The aforementioned focal length calibration coefficient for monitoring equipment refers to the calibration ratio coefficient corresponding to the focal length of the monitoring equipment preset in the attribute database.

[0067] What needs to be explained is the linkage compensation scheme, which refers to linking the monitoring equipment within the field of view of the smart construction site monitoring system to compensate for obstructions by calling upon the monitoring equipment.

[0068] It needs to be explained that, in order to match the linkage compensation scheme, the smart construction site monitoring system establishes an occlusion compensation scheme library based on the monitoring database, calls the predefined occlusion compensation scheme mapping rules in the monitoring database, inputs the comparison results of the occlusion index deviation value of the smart construction site after the adjustment of the monitoring field of view and the monitoring occlusion deviation value threshold, and retrieves and outputs the corresponding linkage compensation scheme.

[0069] It needs to be explained that the process of matching the focal length calibration coefficient of the monitoring equipment involves obtaining the target focal length of the current monitoring equipment based on the precise distance and location information of structural obstructions detected in real time by the millimeter-wave array. If the focal length of the monitoring equipment is greater than the target focal length, a predefined focal length calibration reduction mapping table is called from the monitoring database. The deviation between the focal length of the monitoring equipment and the target focal length is input into the focal length calibration reduction mapping table, and the focal length calibration reduction coefficient of the monitoring equipment is found and output. There are multiple mapping relationships between the deviation between the focal length of the monitoring equipment and the target focal length and the focal length calibration reduction coefficient, including focal length calibration reduction mapping relationships... The embodiment uses a focal length calibration reduction mapping table. If the focal length of the monitoring device is less than the target focal length, a predefined focal length calibration increase mapping table is called from the monitoring database. The deviation between the focal length of the monitoring device and the target focal length is input into the focal length calibration increase mapping table, and the focal length calibration increase coefficient of the monitoring device is found and output. There are multiple mapping relationships between the deviation between the focal length of the monitoring device and the target focal length and the focal length calibration increase coefficient, including the focal length calibration increase mapping table and the focal length calibration increase mapping function. This embodiment uses the focal length calibration increase mapping table.

[0070] The deviation between the focal length of the monitoring device and the target focal length mentioned above refers to the absolute value of the difference between the focal length of the monitoring device and the target focal length.

[0071] The aforementioned focal length calibration reduction mapping function refers to a preset linear function in the monitoring database. Specifically, when the focal length of the monitoring device is greater than the target focal length, the system inputs the deviation between the focal length of the monitoring device and the target focal length into the focal length calibration reduction mapping function. This function calculates the focal length calibration reduction coefficient of the monitoring device by directly multiplying the proportional coefficient of the focal length calibration reduction coefficient preset in the monitoring database with the deviation between the focal length of the monitoring device and the target focal length.

[0072] The aforementioned focal length calibration amplification mapping function refers to a preset linear function in the monitoring database. Specifically, when the focal length of the monitoring device is less than the target focal length, the system inputs the deviation between the focal length of the monitoring device and the target focal length into the focal length calibration amplification mapping function. This function calculates the focal length calibration amplification factor of the monitoring device by directly multiplying the proportional coefficient of the preset focal length calibration amplification factor in the monitoring database with the deviation between the focal length of the monitoring device and the target focal length.

[0073] It should be explained that the focal length of the monitoring equipment is calibrated by the focal length calibration coefficient. If the focal length of the monitoring equipment is greater than the target focal length, the focal length is calibrated by multiplying the focal length by the focal length calibration reduction coefficient; if the focal length of the monitoring equipment is less than the target focal length, the focal length is calibrated by multiplying the focal length by the focal length calibration increase coefficient.

[0074] Specifically, the second compensation scheme is used to adjust the secondary occlusion compensation of the smart construction site. The specific adjustment process is as follows: The second compensation scheme refers to a linear compensation scheme that matches the occlusion index deviation value of the smart construction site's monitoring field of view after the adjustment with the monitoring occlusion deviation value threshold. At the same time, the focal length calibration coefficient of the monitoring equipment is matched from the monitoring database based on the millimeter-wave radar array detection data, and the focal length of the monitoring equipment is calibrated through the focal length calibration coefficient of the monitoring equipment.

[0075] It should be explained that the matching process in the second compensation scheme, which involves matching the focal length calibration coefficient of the monitoring equipment from the monitoring database based on the millimeter-wave radar array detection data, is the same as the matching process in the first compensation scheme.

[0076] It needs to be explained that the linear compensation scheme refers to the smart construction site monitoring system compensating for the obstructed areas of the smart construction site by acquiring and integrating historical monitoring data of the smart construction site.

[0077] It needs to be explained that, in order to match a linear compensation scheme, the smart construction site monitoring system establishes an occlusion compensation scheme library based on the monitoring database, calls the predefined occlusion compensation scheme mapping rules in the monitoring database, inputs the comparison results of the occlusion index deviation value of the smart construction site after increasing and adjusting the monitoring field of view and the monitoring occlusion deviation value threshold, and retrieves and outputs the corresponding linear compensation scheme.

[0078] It should be explained that the occlusion compensation scheme library stores occlusion compensation schemes for smart construction site intelligent monitoring systems based on AR and multi-source data fusion.

[0079] In one specific embodiment, the present invention provides a smart construction site intelligent monitoring system based on AR and multi-source data fusion. By utilizing AR technology and fusing multi-source data, a smart construction site is constructed. The system collects and analyzes the occlusion parameters of the monitoring field of view of the smart construction site and adjusts the AR monitoring of the smart construction site for occlusion compensation. Compared with traditional monitoring methods, this AR-based monitoring method can monitor the construction site more comprehensively and respond to sudden risks more quickly and effectively.

[0080] The optimization module is used to collect and analyze the accuracy indicators of smart construction site monitoring to determine whether the attribute parameters of the smart construction site can be collected. If the attribute parameters of the smart construction site cannot be collected, the AR monitoring of the smart construction site is optimized. If the attribute parameters of the smart construction site can be collected, the attribute parameters of the smart construction site are analyzed.

[0081] Reference Figure 6 As shown: First, monitoring accuracy indicators are collected and the monitoring accuracy index is calculated. Then, the monitoring accuracy index is compared with the monitoring accuracy threshold. If the monitoring accuracy index is greater than or equal to the threshold, the attribute parameters of the smart construction site are analyzed. If the monitoring accuracy index is less than the threshold, it is determined that the attribute parameters of the smart construction site cannot be collected. The AR monitoring is optimized first, then the maximum scanning frequency is matched and the radar scanning frequency is adjusted. After that, it is judged again whether the adjusted monitoring accuracy index is greater than or equal to the monitoring accuracy threshold. This forms a closed-loop optimization process for monitoring accuracy to ensure that the monitoring effect meets the requirements.

[0082] Specifically, the process for determining whether attribute parameters of a smart construction site can be collected is as follows: Analyze the monitoring accuracy indicators of the smart construction site to obtain a monitoring accuracy index, and compare it with the monitoring accuracy threshold stored in the database. If the monitoring accuracy index of the smart construction site is greater than or equal to the monitoring accuracy threshold, it is determined that the attribute parameters of the smart construction site can be collected. If the monitoring accuracy index of the smart construction site is less than the monitoring accuracy threshold, it is determined that the attribute parameters of the smart construction site cannot be collected, and the AR monitoring of the smart construction site is optimized.

[0083] The aforementioned monitoring accuracy threshold refers to the minimum permissible value of the smart construction site monitoring accuracy index preset in the monitoring database.

[0084] The accurate monitoring indicators for smart construction sites include the harmonics of strong electrical interference, the fusion factor of multi-source monitoring data, and the occlusion index of the monitoring field of view. The aforementioned high-voltage interference harmonics refer to the interference harmonics generated by the operation of high-voltage equipment in smart construction sites; the aforementioned multi-source monitoring data fusion factor is a factor that determines the reliability of the fusion of camera monitoring data and monitoring equipment monitoring data; among them, high-voltage interference harmonics can be directly monitored and obtained through a power analyzer.

[0085] It should be explained that high-voltage electrical equipment includes tower crane frequency converters, welding machine groups, and concrete vibrators, which are devices that cause continuous pollution from transient impacts during start-up and shutdown or from nonlinear loads. It should also be explained that interference harmonics are sinusoidal components that are integer multiples of the fundamental wave of the transient impacts during start-up and shutdown or from the continuous pollution from nonlinear loads of high-voltage electrical equipment.

[0086] The aforementioned transient impact during start-up and shutdown refers to the current interference impact caused by high-power electromechanical equipment at the moment of start-up or shutdown; the aforementioned continuous pollution from nonlinear loads refers to the continuous pollution from current interference caused by the distortion of the current conduction angle during the working cycle of nonlinear high-power equipment.

[0087] It needs to be explained that the multi-source monitoring data fusion factor is a core indicator in the smart construction site monitoring system for quantifying the collaborative value of multi-source data. By introducing contribution coefficients, the factors contributing to the multi-source monitoring data fusion factor are quantified as follows: the overlap between real-time sensing data and static model data, the overlap between static model data and historical data, and the overlap between historical data and real-time sensing data. These contribution coefficients are then aggregated to derive the multi-source monitoring data fusion factor for the smart construction site. The specific expression for the multi-source monitoring data fusion factor is as follows: ; In the formula, AF is the multi-source monitoring data fusion factor of the smart construction site, R1 is the building overlap between the real-time sensing data and the static model data of the smart construction site, R2 is the building overlap between the static model data and the historical data of the smart construction site, R3 is the building overlap between the historical data and the real-time sensing data of the smart construction site, δ1 is the contribution coefficient corresponding to the building overlap factor between the real-time sensing data and the static model data of the smart construction site, δ2 is the contribution coefficient corresponding to the building overlap factor between the static model data and the historical data of the smart construction site, and δ3 is the contribution coefficient corresponding to the building overlap factor between the historical data and the real-time sensing data of the smart construction site.

[0088] The building overlap between the real-time sensing data and the static model data of the aforementioned smart construction site represents the degree of overlap between the real-time sensing data and the static model. This can be obtained through point cloud registration technology. First, the features (such as key points, normal vectors, and feature descriptions) of the real-time point cloud generated from the real-time sensing data and the pre-defined point cloud of the static model data are extracted. Then, feature matching is performed to determine the corresponding point pairs. Next, the rigid body transformation matrix (including rotation and translation parameters) is estimated based on the matched point pairs, and the real-time point cloud is transformed into the static model coordinate system. Finally, the building overlap between the two is quantified by calculating the proportion of the number of overlapping points between the real-time point cloud and the static model point cloud to the total point cloud or the proportion of the overlapping area volume. The building overlap between the static model data and historical data of the aforementioned smart construction site represents the degree of overlap between the static model data and historical data, and can be obtained through point cloud registration technology. The building overlap between the historical data and real-time sensing data of the aforementioned smart construction site represents the degree of overlap between the historical data and the real-time sensing data, and can be obtained through point cloud registration technology.

[0089] The contribution coefficients corresponding to the building overlap factors of the aforementioned real-time sensing data and static model data are preset in the monitoring database, with a value range of (0, 1]. The contribution coefficients corresponding to the building overlap factors of the aforementioned smart construction site's static model data and historical data are preset in the monitoring database, with a value range of (0, 1). The contribution coefficients corresponding to the building overlap factors of the aforementioned smart construction site's historical data and real-time sensing data are preset in the monitoring database, with a value range of (0, 1).

[0090] By introducing contribution coefficients, the proportional relationship between strong electrical interference harmonics and strong electrical interference harmonics in smart construction sites, the proportional relationship between the multi-source monitoring data fusion factor of smart construction sites and the multi-source monitoring data fusion factor, and the degree of monitoring view occlusion threshold and monitoring view occlusion index of smart construction sites are quantified and defined. These contribution coefficients are then aggregated to derive the monitoring accuracy index of smart construction sites. The specific expression for the monitoring accuracy index of smart construction sites is as follows: ; In the formula, MAI is the monitoring accuracy index of the smart construction site, z is the high-voltage interference harmonics of all high-voltage equipment in the smart construction site, AF is the multi-source monitoring data fusion factor of the smart construction site, SOI is the monitoring field occlusion index of the smart construction site, z_max is the pre-defined high-voltage interference harmonics in the monitoring database, AF_min is the pre-defined multi-source monitoring data fusion factor in the monitoring database, SOI_MAX is the pre-defined monitoring field occlusion threshold in the monitoring database, λ is the contribution coefficient corresponding to the pre-defined high-voltage interference harmonics in the monitoring database, μ is the contribution coefficient corresponding to the pre-defined multi-source monitoring data fusion factor in the monitoring database, and η is the contribution coefficient corresponding to the pre-defined monitoring field occlusion index in the monitoring database.

[0091] It should be explained that the multi-source monitoring data fusion factor of the smart construction site is updated in real time. The multi-source monitoring data fusion factor of the smart construction site mentioned above is the latest multi-source monitoring data fusion factor calculated and updated by the smart construction site monitoring system.

[0092] The aforementioned monitoring accuracy index for smart construction sites is a quantitative indicator that constitutes the monitoring accuracy of smart construction sites after compensation and occlusion adjustment. It is used to characterize the accuracy of the monitoring occlusion compensation adjustment for smart construction sites.

[0093] The above definition of strong electrical interference harmonics represents the maximum permissible value of strong electrical interference harmonics preset in the monitoring database; the above definition of multi-source monitoring data fusion factor represents the minimum permissible value of multi-source monitoring data fusion factor preset in the monitoring database; the above monitoring field occlusion threshold represents the monitoring field occlusion index threshold, which is the maximum permissible value of the monitoring field occlusion index preset in the monitoring database.

[0094] The contribution coefficient corresponding to the aforementioned strong electrical interference harmonics represents the contribution of the ratio between the strong electrical interference harmonics and the strong electrical interference harmonics of the smart construction site to the monitoring accuracy index. It is preset in the monitoring database and has a value range of (0, 1]. The contribution coefficient corresponding to the aforementioned multi-source monitoring data fusion factor represents the contribution of the ratio between the multi-source monitoring data fusion factor of the smart construction site and the multi-source monitoring data fusion factor to the monitoring accuracy index. It is preset in the monitoring database and has a value range of (0, 1). The contribution coefficient corresponding to the ratio between the monitoring field occlusion threshold and the monitoring field occlusion index of the smart construction site represents the contribution of the monitoring field occlusion index of the smart construction site to the monitoring accuracy index. It is preset in the monitoring database and has a value range of (0, 1).

[0095] It should be explained that the increase in high-voltage interference harmonics means that the start-up, shutdown or operation of high-voltage equipment in smart construction sites increases the interference to monitoring equipment, thereby reducing the accuracy of monitoring data collected by multi-modal equipment, lowering the monitoring accuracy index, and reducing the multi-source monitoring data fusion factor. In other words, the increase in high-voltage interference harmonics leads to a decrease in the monitoring accuracy index, a decrease in the multi-source monitoring data fusion factor, and a further decrease in the monitoring accuracy index. The decrease in the ratio between the defined high-voltage interference harmonics and the high-voltage interference harmonics of the smart construction site leads to a decrease in the monitoring accuracy index. Conversely, an increase in the ratio between the multi-source monitoring data fusion factor of the smart construction site and the defined multi-source monitoring data fusion factor leads to a decrease in the monitoring accuracy index. On the other hand, an increase in the multi-source monitoring data fusion factor, and an increase in the ratio between the multi-source monitoring data fusion factor of the smart construction site and the defined multi-source monitoring data fusion factor, with other parameters remaining constant, leads to an increase in the monitoring accuracy index. An increase in the monitoring field of view occlusion index, and a decrease in the ratio between the monitoring field of view occlusion threshold and the monitoring field of view occlusion index of the smart construction site, with other parameters remaining constant, leads to a decrease in the monitoring accuracy index. In summary, high-voltage interference harmonics indirectly affect the monitoring accuracy index by influencing the multi-source monitoring data fusion factor. Simultaneously, high-voltage interference harmonics, the multi-source monitoring data fusion factor, and the monitoring field of view occlusion index directly affect the monitoring accuracy index, ultimately impacting it.

[0096] Reference Figure 7As shown: First, the system initiates the optimization process by adjusting the radar scanning frequency. Then, it immediately checks whether the adjusted Monitoring Accuracy Index (MAI) reaches the preset monitoring accuracy threshold. If the condition is met, the system enters continuous operation and simultaneously analyzes attribute parameters to maintain stable monitoring. If the threshold is not met, a power matching mechanism is triggered. The system obtains the deviation value of the current MAI by matching the maximum power and adjusts the radar power accordingly. After power adjustment, the system checks again whether the deviation value of the MAI after the second adjustment reaches the deviation threshold requirement. If it meets the standard, continuous operation resumes; if it still does not meet the standard, it escalates to a level-two risk warning, indicating the need for manual intervention or higher-level adjustment measures. The entire process, through dual frequency and power adjustments and multi-level verification, forms a closed-loop monitoring optimization and risk control system.

[0097] Furthermore, the AR monitoring of the smart construction site is optimized. The specific optimization process is as follows: Based on the monitoring accuracy index and monitoring accuracy threshold of the smart construction site, the maximum scanning frequency of the millimeter-wave radar is determined by matching the monitoring database; the scanning frequency of the millimeter-wave radar is adjusted by defining the maximum scanning frequency to achieve one adjustment; the monitoring accuracy index of the smart construction site after the adjustment is obtained again and compared with the monitoring accuracy threshold. If the monitoring accuracy index of the smart construction site after the adjustment is greater than or equal to the monitoring accuracy threshold, the millimeter-wave radar continues to be transmitted at the current scanning frequency; if the monitoring accuracy index of the smart construction site after the adjustment is less than the monitoring accuracy threshold, the deviation value of the monitoring accuracy index of the smart construction site after the adjustment is obtained and compared with the monitoring data. The system compares the monitoring accuracy deviation value with a preset threshold value in the database. If the monitoring accuracy deviation value of the smart construction site after the first adjustment is less than or equal to the monitoring accuracy deviation value threshold, the system continues to transmit millimeter-wave radar at the current scanning frequency. If the monitoring accuracy deviation value of the smart construction site after the first adjustment is greater than the monitoring accuracy deviation value threshold, the system matches the maximum power of the millimeter-wave radar from the monitoring database. The system adjusts the millimeter-wave radar power by defining the maximum power to achieve a second adjustment. The system then obtains the monitoring accuracy index of the smart construction site after the second adjustment and compares it with the monitoring accuracy threshold. If the monitoring accuracy index of the smart construction site after the second adjustment is greater than or equal to the monitoring accuracy threshold, the system continues to transmit millimeter-wave radar at the current scanning frequency and power.

[0098] If the monitoring accuracy index of the smart construction site after the second adjustment is still less than the monitoring accuracy threshold, a level-two monitoring risk warning will be issued immediately.

[0099] The aforementioned monitoring accuracy threshold refers to the preset monitoring accuracy index in the monitoring database, which is the minimum permissible value of the preset monitoring accuracy index in the monitoring database.

[0100] The deviation value of the monitoring accuracy index of the smart construction site after the above adjustment refers to the preset defined monitoring accuracy index in the monitoring database minus the monitoring accuracy index of the smart construction site after the first adjustment.

[0101] The aforementioned monitoring accuracy deviation threshold refers to the preset monitoring accuracy index deviation value in the monitoring database, which is the maximum permissible value for the preset monitoring accuracy deviation value in the monitoring database.

[0102] It needs to be explained that the maximum scanning frequency defined by the millimeter-wave radar is matched, the predefined maximum scanning frequency mapping rules in the monitoring database are called, the monitoring accuracy index and monitoring accuracy threshold of the smart construction site are input, and the maximum scanning frequency defined by the millimeter-wave radar is retrieved and output.

[0103] It needs to be explained that the maximum power defined by the millimeter-wave radar is matched, the predefined maximum power mapping rules in the monitoring database are called, the monitoring accuracy index and monitoring accuracy threshold of the smart construction site after one adjustment are input, and the maximum power defined by the millimeter-wave radar is retrieved and output.

[0104] It should be explained that the Level 2 monitoring risk warning means that the smart construction site monitoring system will pop up a window in the administrator's window and show that there is a serious risk in the current monitoring, and immediately send a work stoppage notice.

[0105] In one specific embodiment, the present invention, based on the judgment and optimization module of the monitoring field occlusion index and the monitoring accuracy index, collects the monitoring accuracy indicators of the smart construction site in real time. Combined with the secondary coupling of monitoring field occlusion, the system can evaluate the accuracy of monitoring occlusion compensation. Based on the evaluated monitoring occlusion compensation, the system can achieve adaptive updating and optimization of monitoring occlusion compensation. This realizes closed-loop dynamic optimization of AR compensation strategy and adaptive collaboration of multi-source data contribution, providing a more comprehensive and accurate intelligent monitoring and occlusion compensation solution for smart construction sites, and providing strong support for building a safer modern construction site.

[0106] The adjustment and early warning module is used to analyze the attribute parameters of the smart construction site, obtain the project progress of the smart construction site, and locate abnormal areas of the smart construction site, so as to issue early warning prompts.

[0107] Reference Figure 8 As shown: First, the attribute parameters are analyzed, and then the adjustment and early warning module is entered. First, multi-source data is integrated, then the abnormal area benchmark is matched, then the abnormal area is marked and an early warning prompt is sent, and then the database is updated. At the same time, the adjustment and early warning module also obtains the project progress. Finally, the process ends. Overall, the abnormality is identified, early warning is issued and the data is updated by integrating multi-source data, and the abnormality monitoring and early warning management of the smart construction site is completed in combination with the project progress.

[0108] Specifically, the abnormal areas of the smart construction site are located. The specific location process is as follows: based on the attribute parameters of the smart construction site and the monitoring data of the smart construction site, the multi-source data is integrated and dynamically matched with the preset abnormal area benchmark in the monitoring database; the matching result of the abnormal area benchmark is obtained, and the abnormal area of ​​the smart construction site is marked.

[0109] The aforementioned attribute parameters of the smart construction site include data on the mechanical equipment, engineering materials, and construction process of the smart construction site.

[0110] The aforementioned monitoring data for smart construction sites includes the occlusion parameters of the monitoring field of view, the accuracy indicators of the monitoring, and the monitoring area of ​​the smart construction site.

[0111] It needs to be explained that obtaining the project progress of a smart construction site involves real-time updates of the monitoring screen in the construction area via AR monitoring, real-time collection and analysis of the smart construction site's attribute parameters and monitoring data, data fusion of the smart construction site's attribute parameters and monitoring data, and transmission to the analysis layer of the smart construction site monitoring system for analysis. The construction scene is overlaid with the BIM model in real-time via AR monitoring, allowing for a direct comparison and analysis of progress deviations. First, a lightweight BIM model is constructed and on-site positioning markers are deployed, or natural feature recognition algorithms are used. AR devices are then used to accurately overlay the actual construction scene with the BIM model, intuitively displaying the deviation between actual progress and the plan. The project progress of the smart construction site is obtained based on the analysis results.

[0112] The aforementioned natural feature recognition algorithm is a spatial positioning method based on computer vision and artificial intelligence technologies. It captures high-definition images or point cloud data of the construction scene of a smart construction site in real time through AR devices. After denoising the data table, it identifies stable feature points in the high-definition images or point cloud data, generates feature descriptors, quantifies the local appearance information of stable feature points, and determines the positioning location in the smart construction site.

[0113] It needs to be explained that AR devices accurately overlay the actual construction scene onto the BIM model, break down the BIM model into independently loadable modular components, establish a conversion relationship between the global coordinate system and the engineering measurement coordinate system, and deploy AR positioning markers with unique codes at key locations in the construction area. The markers contain visual recognition patterns and near-field communication chips. The precise coordinates of the markers in the engineering coordinate system are measured and marked in the global coordinate system, or environmental feature points are marked in the global coordinate system through natural feature recognition algorithms. This enables AR devices to accurately overlay the actual construction scene onto the BIM model.

[0114] It needs to be explained that to obtain the matching results of the abnormal area benchmark, the multi-source sensors integrated on the safety helmet collect the attribute parameters of the construction site in real time. Combined with the cloud BIM model, the attribute parameters based on the smart construction site and the monitoring data of the smart construction site are fused. The fused data is input into the monitoring database of the smart construction site. The monitoring database marks the fused data that is greater than the benchmark corresponding to the abnormal area as abnormal data, thus completing the abnormal area benchmark matching.

[0115] The aforementioned cloud-based BIM model refers to a building information model built based on cloud computing technology.

[0116] It needs to be explained that marking abnormal areas in a smart construction site involves acquiring monitoring data from the marked abnormal data source based on multi-source data. The system performs deep correlation and spatial mapping on the marked abnormal data, and analyzes the effective visible range of the monitoring data by combining the occlusion parameters of the monitoring field of view included in the abnormal data. Through spatial computing and data fusion technology, the data points of the discrete abnormal data are located to specific physical spatial coordinates, and the monitoring area included in the abnormal data is superimposed. This process analyzes and identifies the monitoring area of ​​the marked abnormal data in the smart construction site, and marks it as an abnormal area.

[0117] The second aspect of the present invention provides a smart construction site monitoring system based on AR and multi-source data fusion, including: AR glasses and multimodal sensors.

[0118] The aforementioned AR glasses refer to dynamic occlusion compensation devices for smart construction sites. They are used to compensate for occlusion in the monitoring of smart construction sites and to collect and transmit occlusion parameters in the monitoring field of view. Through augmented reality technology, they generate dynamic compensation images of the occluded areas in real time, and scan the three-dimensional structural data of the surrounding environment of the occluded area in real time (such as point cloud information obtained through LiDAR). Combined with SLAM technology, they construct a local spatial model. At the same time, the edge computing unit extracts features (such as texture and color distribution) from the historical monitoring images before occlusion and matches them with the currently scanned three-dimensional model to generate a virtual compensation image of the occluded area. The aforementioned multimodal sensors synchronously collect monitoring data and spatial coordinate information within the monitoring field of view.

[0119] The aforementioned edge computing unit refers to the core hub connecting the front-end sensing device and the back-end data processing. It integrates a high-performance embedded processor, a high-speed cache module, and a multi-interface communication module, and can be directly deployed on AR glasses terminals or edge nodes near construction sites.

[0120] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A smart construction site intelligent monitoring system based on AR and multi-source data fusion, characterized in that, include: The analysis and judgment module is used to utilize AR technology and fuse multi-source data to build a smart construction site. It collects and analyzes the occlusion parameters of the monitoring field of view of the smart construction site to determine whether to adjust the occlusion compensation for the AR monitoring of the smart construction site. The optimization module is used to collect and analyze the accuracy indicators of the smart construction site monitoring to determine whether the attribute parameters of the smart construction site can be collected. If the attribute parameters of the smart construction site cannot be collected, the AR monitoring of the smart construction site will be optimized. After the optimization meets the standards, the attribute parameters of the smart construction site will be collected and analyzed. If the attribute parameters of the smart construction site can be collected, the attribute parameters of the smart construction site will be analyzed. The adjustment and early warning module is used to analyze the attribute parameters of the smart construction site, obtain the project progress of the smart construction site, and locate abnormal areas of the smart construction site, so as to issue early warning prompts.

2. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 1, characterized in that: The specific determination process for whether to adjust the AR monitoring of the smart construction site for occlusion compensation is as follows: The occlusion parameters of the monitoring field of smart construction sites are analyzed to obtain the occlusion index of the monitoring field of smart construction sites, and then compared with the occlusion threshold of the monitoring field stored in the database. If the occlusion index of the monitoring field of view of the smart construction site is less than or equal to the occlusion threshold of the monitoring field of view, it is determined that no occlusion compensation adjustment will be made for the AR monitoring of the smart construction site. If the occlusion index of the monitoring field of the smart construction site is greater than the occlusion threshold of the monitoring field of the smart construction site, it is determined that the AR monitoring of the smart construction site will be adjusted for occlusion compensation and the millimeter-wave radar array will be activated at the reference scanning frequency. The occlusion index of the smart construction site's monitoring field of view is used to characterize the degree of occlusion of the monitoring within the monitoring field of view of the smart construction site. The occlusion parameters of the monitoring field of view of a smart construction site include the dust extinction coefficient of the smart construction site, the dynamic occlusion area of ​​the smart construction site, and the camera field of view deflection of the smart construction site.

3. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 2, characterized in that: The specific adjustment process for occlusion compensation in the AR monitoring of smart construction sites is as follows: The type of occlusion in a smart construction site is determined based on the detection results of a millimeter-wave radar array. If it is determined to be diffuse occlusion, an environmental risk warning is issued directly. If it is determined to be structural occlusion, the scanning frequency amplification coefficient is matched from the monitoring database based on the occlusion index and occlusion threshold of the monitoring field of the smart construction site, and the scanning frequency of the millimeter-wave radar array of the smart construction site is increased and adjusted by the scanning frequency amplification coefficient. After the adjustment is completed, obtain the occlusion index of the monitoring field of the smart construction site and compare it with the occlusion threshold of the monitoring field to determine whether to perform secondary occlusion compensation adjustment for AR monitoring.

4. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 3, characterized in that: The specific determination process for whether to perform secondary occlusion compensation adjustment on AR monitoring is as follows: The occlusion index of the smart construction site's monitoring field of view after the adjustment is increased is compared with the occlusion threshold of the monitoring field of view. If the occlusion index of the smart construction site's monitoring field of view after the adjustment is increased is less than or equal to the occlusion threshold of the monitoring field of view, it is determined that no secondary occlusion compensation adjustment will be performed on the AR monitoring. If the occlusion index of the smart construction site's monitoring field of view is still greater than the occlusion threshold after the adjustment is increased, it is determined that a second occlusion compensation adjustment will be performed on the AR monitoring. The specific compensation adjustment process is as follows: obtain the deviation value of the occlusion index of the smart construction site's monitoring field of view after the adjustment is increased, and compare it with the preset monitoring occlusion deviation value threshold in the monitoring database. If the deviation value of the occlusion index of the smart construction site's monitoring field of view after the adjustment is increased is less than or equal to the monitoring occlusion deviation value threshold, then a second occlusion compensation adjustment will be performed on the smart construction site through the first compensation scheme. If the deviation value of the occlusion index of the smart construction site after the adjustment is increased is greater than the threshold value of the occlusion deviation value, then the smart construction site will be adjusted for secondary occlusion compensation through the second compensation scheme, and a first-level monitoring risk warning will be issued at the same time. The monitoring occlusion deviation threshold refers to the maximum permissible value of the monitoring field occlusion index deviation value preset in the monitoring database.

5. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 4, characterized in that: The second occlusion compensation adjustment of the smart construction site through the first compensation scheme is as follows: The first compensation scheme refers to a linkage compensation scheme based on the increased and adjusted occlusion index deviation value of the smart construction site and the monitoring occlusion deviation value threshold, and at the same time, the monitoring equipment focal length calibration coefficient is matched based on the detection results of the millimeter-wave radar array, and the focal length of the monitoring equipment is calibrated through the monitoring equipment focal length calibration coefficient. The focal length calibration coefficient of the monitoring equipment refers to the calibration ratio coefficient corresponding to the focal length of the monitoring equipment preset in the attribute database. The aforementioned linkage compensation scheme refers to linking the monitoring equipment within the field of view of the smart construction site monitoring system to perform occlusion compensation by calling upon the monitoring equipment.

6. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 4, characterized in that: The second compensation scheme is used to perform secondary occlusion compensation adjustment on the smart construction site. The specific adjustment process is as follows: The second compensation scheme refers to a linear compensation scheme based on matching the deviation value of the occlusion index of the smart construction site's monitoring field of view after the adjustment with the threshold value of the monitoring occlusion value. At the same time, the focal length calibration coefficient of the monitoring equipment is matched from the monitoring database based on the detection results of the millimeter-wave radar array, and the focal length of the monitoring equipment is calibrated through the focal length calibration coefficient of the monitoring equipment. The linear compensation scheme refers to the smart construction site monitoring system compensating for obstructed areas of the smart construction site by acquiring and integrating historical monitoring data of the smart construction site.

7. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 1, characterized in that: The specific process for determining whether the attribute parameters of the smart construction site can be collected is as follows: Analyze the monitoring accuracy indicators of smart construction sites to obtain the monitoring accuracy index of smart construction sites, and compare it with the monitoring accuracy thresholds stored in the database. If the monitoring accuracy index of the smart construction site is greater than or equal to the monitoring accuracy threshold, it is determined that the attribute parameters of the smart construction site can be collected and analyzed. If the monitoring accuracy index of the smart construction site is less than the monitoring accuracy threshold, it is determined that the attribute parameters of the smart construction site cannot be collected, and the AR monitoring of the smart construction site will be optimized. The monitoring accuracy index of the smart construction site is used to characterize the accuracy of the smart construction site monitoring occlusion compensation adjustment. The accurate monitoring indicators for smart construction sites include the harmonics of strong electrical interference, the fusion factor of multi-source monitoring data, and the occlusion index of the monitoring field of view.

8. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 1, characterized in that: The optimization process for AR monitoring of smart construction sites is as follows: Based on the monitoring accuracy index and monitoring accuracy threshold of the smart construction site, the maximum scanning frequency of the millimeter-wave radar is determined by matching the monitoring database. The scanning frequency of the millimeter-wave radar is adjusted by defining the maximum scanning frequency to achieve one adjustment. The monitoring accuracy index of the smart construction site after the adjustment is obtained again and compared with the monitoring accuracy threshold. If the monitoring accuracy index of the smart construction site after the adjustment is greater than or equal to the monitoring accuracy threshold, the millimeter-wave radar will continue to be transmitted at the current scanning frequency. If the monitoring accuracy index of the smart construction site after one adjustment is less than the monitoring accuracy threshold, the deviation value of the monitoring accuracy index of the smart construction site after one adjustment is obtained and compared with the preset monitoring accuracy deviation value threshold in the monitoring database. If the deviation value of the monitoring accuracy index of the smart construction site after one adjustment is less than or equal to the monitoring accuracy deviation value threshold, the millimeter-wave radar will continue to be transmitted through the current scanning frequency. If the monitoring accuracy index deviation value of the smart construction site after one adjustment is greater than the monitoring accuracy deviation value threshold, the maximum power of the millimeter-wave radar will be matched from the monitoring database. The millimeter-wave radar power is adjusted by defining the maximum power to achieve a secondary adjustment. The monitoring accuracy index of the smart construction site after the secondary adjustment is obtained again and compared with the monitoring accuracy threshold. If the monitoring accuracy index of the smart construction site after the secondary adjustment is greater than or equal to the monitoring accuracy threshold, the millimeter-wave radar will continue to be transmitted through the current scanning frequency and power. If the monitoring accuracy index of the smart construction site is still less than the monitoring accuracy threshold after the second adjustment, a level-two monitoring risk warning will be issued immediately. The monitoring accuracy threshold refers to the preset monitoring accuracy index in the monitoring database, which is the minimum permissible value of the preset monitoring accuracy index in the monitoring database. The deviation value of the monitoring accuracy index of the smart construction site after the first adjustment refers to the preset defined monitoring accuracy index in the monitoring database minus the monitoring accuracy index of the smart construction site after the first adjustment. The monitoring accuracy deviation threshold refers to the preset monitoring accuracy index deviation value in the monitoring database, which is the maximum permissible value of the preset monitoring accuracy deviation value in the monitoring database.

9. The intelligent monitoring system for smart construction sites based on AR and multi-source data fusion as described in claim 1, characterized in that: The specific process for locating the abnormal areas of the smart construction site is as follows: Based on the attribute parameters and monitoring data of the smart construction site, multi-source data are integrated and dynamically matched with the preset abnormal area benchmark in the monitoring database. Obtain the matching results of the abnormal area benchmark and mark the abnormal areas of the smart construction site; The monitoring data of the smart construction site includes the occlusion parameters of the monitoring field of view of the smart construction site, the monitoring accuracy indicators of the smart construction site, and the monitoring area of ​​the smart construction site.

10. The device for the intelligent monitoring system of smart construction site based on AR and multi-source data fusion as described in any one of claims 1-9, characterized in that: include: AR glasses, multimodal sensors; The AR glasses refer to the dynamic occlusion compensation device for smart construction sites, which is used to compensate for the occlusion of monitoring in smart construction sites and to collect and transmit occlusion parameters of the monitoring field of view to generate dynamic compensation images of the occluded area in real time through augmented reality technology. The multimodal sensor synchronously collects monitoring data and spatial coordinate information within the monitoring field of view.

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