Intelligent construction engineering operation and maintenance management image information system

Abstract

The invention discloses an intelligent construction engineering operation and maintenance management image information system, and relates to the technical field of engineering operation and maintenance management. An intelligent construction project operation and maintenance management image information system comprises a data acquisition system, an operation and maintenance management system and a safety monitoring system. The problems that in the prior art, constructors cannot be managed, potential safety hazards of the constructors are likely to occur, and meanwhile the construction environment and equipment energy consumption cannot be monitored and managed are solved, the safety and energy utilization efficiency of a construction site are effectively improved, power equipment faults can be found and solved in time, and the safety of the construction site is improved. The construction management optimization is performed from the perspective of green low-carbon construction while energy conservation and emission reduction are completed, the comprehensive coordination optimization of the construction management of the project is realized, the real-time construction safety monitoring and early warning are realized, the working risk is reduced, and the construction quality is improved. And the management convenience and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering operation and maintenance management, and in particular to an intelligent construction engineering operation and maintenance management image information system. Background Art

[0002] In the traditional bridge construction process, construction management usually faces many challenges. These challenges include but are not limited to the difficulty of real-time monitoring of construction progress, lack of adaptability to complex construction environments, and accurate assessment of structural safety. At the same time, traditional management methods also have challenges in material use and quality control. Due to the lack of accurate real-time data and efficient analysis tools, it is difficult for engineering teams to identify and solve construction quality problems in a timely manner, which may lead to later structural problems and increased maintenance costs.

[0003] The Chinese patent with publication number CN118194409A discloses a 3D visualization construction management system, including a data acquisition system for collecting real-time data of the bridge construction site, a 3D visualization module for building a 3D model of the bridge, a progress management module for monitoring the progress of the bridge project and adjusting the construction plan in time, a quality control module for checking the structural integrity and material applicability, and a bridge dynamic load response simulation module for simulating and analyzing the impact of various dynamic loads on the bridge during construction and use. The patent can improve management efficiency, accurately monitor the construction status of the construction site, and effectively predict and evaluate the response and stability of the bridge structure.

[0004] In actual use, the above patent cannot manage construction personnel, which may lead to potential safety hazards. At the same time, it cannot monitor and manage the construction environment and equipment energy consumption, which will not only cause economic losses, but also cause environmental pollution due to high energy consumption. Therefore, it does not meet existing needs. We have proposed an intelligent construction project operation and maintenance management image information system. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent construction project operation and maintenance management image information system, which effectively improves the safety and energy utilization efficiency of the construction site, can promptly detect and resolve power equipment failures, reduce the risk of safety accidents, and optimize the power supply plan, improve the construction efficiency of the construction site, and optimize construction management from the perspective of green and low-carbon construction while completing energy conservation and emission reduction, thereby achieving comprehensive coordinated optimization of the construction management of this project, reducing work risks and improving construction quality, improving the convenience and efficiency of management, and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: an intelligent construction engineering operation and maintenance management image information system, comprising:

[0007] Data collection system, used to collect environmental data, meteorological data, multi-dimensional information data of river water dynamics in the construction area, and image data of the main structure and temporary building facilities under construction, and generate a building information model based on the collected data;

[0008] The operation and maintenance management system is used to manage personnel, construction machinery and equipment, temporary power safety status, trestle speed measurement, construction structure and personnel safety, and simulate and adjust the jacking;

[0009] The safety monitoring system is used to monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops, temperature control of large-volume concrete, steel box cofferdams and ice pressure, and perform AI intelligent identification and monitoring of environmental loads.

[0010] Preferably, the data acquisition system comprises:

[0011] Environmental sensors are used to collect environmental data, meteorological data and multi-dimensional information data of the river water dynamics in the construction area. The sensors include temperature and humidity sensors, meteorological sensors and flow rate sensors;

[0012] Image acquisition equipment, used to collect real-time image data of the main structure and temporary construction facilities under construction. The temporary construction facilities include steel trestles and steel box cofferdams. The image acquisition equipment is a high-definition camera;

[0013] The scene generation module is used to generate a building information model based on the data collected by the sensor and image acquisition devices.

[0014] Preferably, the scene generation module includes:

[0015] A storage module is used to store data collected by sensors and image acquisition devices, and transmit the collected image data to the real scene processing module;

[0016] The real scene processing module is used to perform a clear processing on the collected image data and transfer the processed image data to the building information model generation module;

[0017] The building information model generation module is used to generate a building information model from the image data transmitted from the real scene acquisition module, and simulate the data collected by the sensor on the building information model.

[0018] Preferably, the real scene processing module includes:

[0019] An image data extraction module, used for extracting the image data after the image data is processed for clarity;

[0020] A first image parameter information extraction module, used to extract the image parameter information of the image data that has completed the sharpening process as the first image parameter information; wherein the first image parameter information includes the average gradient, contrast value and resolution value of the image data that has completed the sharpening process;

[0021] The first image optimization evaluation parameter acquisition module is used to obtain the first image optimization evaluation parameter using the first image parameter information; wherein the first image optimization evaluation parameter is obtained by the following formula:

[0022]

[0023] Where S represents the first image optimization evaluation parameter; G a01 , D a01 and F a01 represents the average gradient, contrast value and resolution value included in the first image parameter information; G y , D y and F y Indicates the minimum average gradient, contrast value and resolution value allowed under the condition of meeting the image clarity; H max and H min They represent the maximum and minimum grayscale values ​​corresponding to the pixels in the image data after the sharpening process; H z and H p They respectively represent the median and average values ​​of the grayscale values ​​corresponding to the pixels in the image data that has completed the sharpening process;

[0024] An evaluation and adjustment module is used to evaluate and adjust the image quality of the image data that has completed the sharpening processing by using the first image optimization evaluation parameter.

[0025] Preferably, the evaluation and adjustment module includes:

[0026] A comparison module, used for comparing the first image optimization evaluation parameter with a preset image optimization evaluation parameter threshold;

[0027] An image optimization degree determination module, configured to determine that the image optimization degree is poor when the first image optimization evaluation parameter is lower than a preset image optimization evaluation parameter threshold;

[0028] A second image parameter information extraction module is used to retrieve the image parameter information of the image data before the sharpening process as the second image parameter information when the optimization degree of the determined image is poor; wherein the second image parameter information includes the average gradient, contrast value and resolution value of the image data before the sharpening process;

[0029] The image adjustment degree coefficient acquisition module is used to obtain the image adjustment degree coefficient by using the first image parameter information and the second image parameter information; wherein the image adjustment degree coefficient is obtained by the following formula:

[0030]

[0031] Where k represents the image adjustment coefficient; G a01 , D a01 and F a01 represents the average gradient, contrast value and resolution value included in the first image parameter information; G a02 , D a02 and F a02 Indicates the average gradient, contrast value and resolution value included in the second image parameter information;

[0032] A minimum standard value acquisition module is used to retrieve the minimum average gradient, contrast value and resolution value allowed under the condition of meeting the image clarity;

[0033] The standard value adjustment module is used to adjust the minimum average gradient, contrast value and resolution value allowed under the condition of satisfying the image clarity by using the image adjustment degree coefficient, and obtain the adjusted minimum average gradient, contrast value and resolution value; wherein the adjusted minimum average gradient, contrast value and resolution value are obtained by the following formula:

[0034]

[0035] Among them, G yt Indicates the minimum average gradient after adjustment; k is the image adjustment coefficient; G y Indicates the minimum average gradient allowed under the condition of satisfying image clarity; G a01 represents the average gradient contained in the first image parameter information; G a02 represents the average gradient included in the second image parameter information;

[0036]

[0037] Among them, D yt represents the minimum average gradient after adjustment; k is the image adjustment coefficient; D y Indicates the minimum contrast value allowed under the condition of meeting the image clarity; D a0D1 represents the contrast value included in the first image parameter information; D a02 Indicates the contrast value included in the second image parameter information;

[0038]

[0039] Among them, F yt Indicates the minimum resolution value after adjustment; k is the image adjustment coefficient; F y Indicates the minimum resolution value allowed under the condition of meeting the image clarity; F a0D1 Indicates the resolution value included in the first image parameter information; F a02 Indicates the resolution value included in the second image parameter information;

[0040] The image data secondary sharpening processing module is used to perform secondary sharpening processing on the image data based on the adjusted minimum average gradient, contrast value and resolution value as a standard until the average gradient, contrast value and resolution value of the image data reach or exceed the adjusted minimum average gradient, contrast value and resolution value.

[0041] Preferably, the operation and maintenance management system includes:

[0042] The personnel management module is used to carry out worker self-registration, real-name attendance management and personnel file management through the labor real-name management system. The labor real-name management system includes dual-platform control of mobile phones and WEB terminals. The real-name attendance management includes automatic recognition of faces, fingerprints, certificates, etc.; the machinery command module is connected with construction machinery and equipment to collect and manage the location, fuel consumption data, operating status and trajectory information of construction machinery and equipment in real time;

[0043] Temporary power management module, used to monitor the safety status and energy consumption of temporary power consumption of construction machinery and equipment in real time;

[0044] The trestle speed management module is installed on the cameras around the trestle to monitor and manage the speed of vehicles in the trestle area in real time, record and analyze the monitored data, and generate corresponding reports based on the analysis results;

[0045] The construction management module is used to refine the building information model using the work breakdown structure, connect the intelligent test press, universal press and compression and flexure press to the network, and obtain the volume, mixing time, pan consumables details, mix ratio and theoretical concrete formula data of precast components in real time;

[0046] The personnel safety management module is used to intelligently identify and send alarms for workers' wearing of safety helmets, flame monitoring, wearing of work clothes, and intrusion into restricted areas through surveillance cameras and AI intelligent algorithms.

[0047] Preferably, the construction management module includes:

[0048] A progress management module, which combines Unreal Engine with the building information model and refines the building information model according to the work breakdown structure;

[0049] Smart experiment module, which is used to connect intelligent test presses, universal machines and compression and flexure machines to the Internet, and store, analyze, display and push test data, test calendar, test pass rate, test times and laboratory monitoring data;

[0050] The smart mixing module is connected to the mixing station to obtain the volume, mixing time, pan consumables details, mix ratio and theoretical concrete formula data of precast components in real time, and bind all concrete data with the on-site construction site;

[0051] The jacking management module is used to build a jacking digital model, simulate jacking, plan the construction path and design jacking parameters in advance according to the simulation results, collect and analyze the geometric and physical parameter data of the structure and equipment in real time during the jacking process, and dynamically adjust the main beam jacking, jacking and lateral deviation correction through the hydraulic control system according to the analysis results;

[0052] Preferably, the security monitoring system comprises:

[0053] Construction monitoring module, which is used to monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops, mass concrete temperature control and steel box cofferdam through intelligent strain sensors and intelligent temperature sensors;

[0054] The environmental monitoring module is used to monitor ice pressure and perform AI intelligent identification and monitoring of environmental loads.

[0055] Preferably, the environmental monitoring module includes:

[0056] Monitoring ice pressure: Using intelligent strain sensors to monitor the strain and deformation of the steel pipe surface during the icing process, based on the research foundation of the mechanical properties of ice and steel pipes, the stress distribution on the steel pipe surface during the icing process is analyzed through strain analysis, and finite element analysis is used to analyze the impact of the icing process on the bearing performance of the steel pipe, and the safety performance is evaluated;

[0057] AI intelligent identification and monitoring of environmental loads: Real-time measurement of river surface velocity and wave vision, construction of a bridge near-field wave transient liquid surface visual reconstruction algorithm, acquisition of the three-dimensional liquid surface change process of waves in time and space according to the wind speed, setting of structural risk warning values, simulation analysis of the mechanical state of the steel trestle and steel box cofferdam, real-time monitoring of construction safety, and early warning when the structural risk warning value is exceeded;

[0058] Monitoring of carbon emissions in the construction environment: used to monitor carbon emissions according to the construction cycle, analyze the total carbon emissions, real-time carbon emissions, the proportion of carbon emissions in each part, project carbon emissions trends and key information on green and low-carbon management of energy conservation and emission reduction effects, and conduct refined management of uncertain carbon emissions in the construction process based on the analysis results, and visualize them through computer vision.

[0059] Preferably, the construction monitoring module includes:

[0060] Monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops: by arranging special prisms on the steel trestle and steel platform pile tops, using a motor-driven total station to automatically find points to capture the prisms, and monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops;

[0061] Monitor the temperature control of large-volume concrete: select characteristic temperature measurement areas and points on the surface, inside and at different heights of the foundation, deploy temperature sensors, connect automatic collection and transmission equipment, and monitor data changes in real time;

[0062] Monitoring of the steel casing cofferdam: using the pre-buried inclinometer tube to monitor the deep horizontal displacement of the steel casing, using the prism on the top of the casing to monitor the horizontal and vertical displacement of the top of the steel casing, and using the strain gauge to monitor the stress changes of the internal support.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention effectively improves the safety and energy utilization efficiency of the construction site by real-time monitoring of the safety status and energy consumption of temporary electricity use, can promptly discover and resolve power equipment failures, reduce the risk of safety accidents, optimize the power supply plan, and improve the construction efficiency of the construction site. At the same time, the collection and analysis of monitoring data also provides an important reference for green and low-carbon management, and finely manages the uncertain carbon emissions in the construction process, strictly controls the energy consumption and material loss in each construction link, and optimizes construction management from the perspective of green and low-carbon construction while completing energy conservation and emission reduction, thereby achieving comprehensive coordinated optimization of the construction management of this project, realizing real-time construction safety monitoring and early warning, reducing work risks and improving construction quality, and helping managers understand the operating status and production status of the mixing station in real time, thereby improving the convenience and efficiency of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a schematic diagram of the module of the intelligent construction engineering operation and maintenance management image information system of the present invention;

[0066] Figure 2 This is a workflow diagram of the intelligent construction engineering operation and maintenance management image information system of the present invention.

[0067] Figure 3 This is a screenshot of the system interface developed based on the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the attached embodiment of the present invention Figure 1-3 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In order to solve the problem that the existing patents cannot manage construction personnel in actual use, which is prone to potential safety hazards, and cannot monitor and manage the construction environment and equipment energy consumption, which will not only cause economic losses but also cause environmental pollution due to high energy consumption, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0070] An intelligent construction engineering operation and maintenance management image information system, comprising:

[0071] The data acquisition system is used to collect environmental data, meteorological data, multi-dimensional information data of river water dynamics in the construction area, and image data of the main structure and temporary construction facilities of the construction area, and generate a building information model based on the collected data. It not only simulates the changes of day and night and the cycle of the four seasons, but also interacts with the real world in real time, thus realizing a comprehensive perception of the four-dimensional environment of "air-sky-land-water".

[0072] The operation and maintenance management system is used to manage personnel, construction machinery and equipment, temporary power safety status, trestle speed measurement, construction structure and personnel safety, and to simulate and adjust jacking.

[0073] The safety monitoring system is used to monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops, temperature control of large-volume concrete, steel box cofferdams and ice pressure, and perform AI intelligent identification and monitoring of environmental loads.

[0074] Data acquisition system, including:

[0075] Environmental sensors are used to collect environmental data, meteorological data and multi-dimensional information data of river water dynamics in the construction area. The sensors include temperature and humidity sensors, meteorological sensors and flow rate sensors.

[0076] Image acquisition equipment is used to collect real-time image data of the main structure and temporary construction facilities under construction. The temporary construction facilities include steel trestles and steel box cofferdams. The image acquisition equipment is a high-definition camera.

[0077] The scene generation module is used to generate a building information model based on data collected by sensors and image acquisition equipment, simulate the day and night changes and the cycle of the four seasons in the construction area, and accurately reflect the actual construction site of the bridge project by building a real-time dynamic building construction model.

[0078] Scene generation module, including:

[0079] The storage module is used to store the data collected by the sensor and the image acquisition device, and transmit the collected image data to the real scene processing module.

[0080] The real scene processing module is used to clarify the collected image data and transfer the processed image data to the building information model generation module.

[0081] The building information model generation module is used to generate a building information model from the image data transmitted from the real scene acquisition module, and simulate the data collected by the sensor on the building information model.

[0082] Specifically, the real scene processing module includes:

[0083] An image data extraction module, used for extracting the image data after the image data is processed for clarity;

[0084] A first image parameter information extraction module, used to extract the image parameter information of the image data that has completed the sharpening process as the first image parameter information; wherein the first image parameter information includes the average gradient, contrast value and resolution value of the image data that has completed the sharpening process;

[0085] The first image optimization evaluation parameter acquisition module is used to obtain the first image optimization evaluation parameter using the first image parameter information; wherein the first image optimization evaluation parameter is obtained by the following formula:

[0086]

[0087] Where S represents the first image optimization evaluation parameter; G a01 , D a01 and F a01 represents the average gradient, contrast value and resolution value included in the first image parameter information; G y , D y and F y Indicates the minimum average gradient, contrast value and resolution value allowed under the condition of meeting the image clarity; H max and H min They represent the maximum and minimum grayscale values ​​corresponding to the pixels in the image data after the sharpening process; Hz and H p They respectively represent the median and average values ​​of the grayscale values ​​corresponding to the pixels in the image data that has completed the sharpening process;

[0088] An evaluation and adjustment module is used to evaluate and adjust the image quality of the image data that has completed the sharpening processing by using the first image optimization evaluation parameter.

[0089] The technical effect of the above technical solution is: by comprehensively considering multiple image parameter information such as the average gradient of the image (representing the clarity or detail richness of the image), the contrast value (reflecting the difference between the light and dark areas in the image) and the resolution value (the fineness of the image details), combined with a specific mathematical formula to calculate the first image optimization evaluation parameter (S), this method is more comprehensive and accurate than a single parameter evaluation, and can more objectively reflect the overall quality of the image. Through the evaluation and adjustment module, the calculated first image optimization evaluation parameter (S) is used to evaluate the quality of the image data that has completed the clarity processing. If the evaluation result shows that the image quality does not meet the preset standard, the system can automatically trigger further image optimization processing, such as enhancing contrast, improving resolution or further sharpening, so as to achieve intelligent adjustment and optimization of image quality.

[0090] The design of the image data extraction module and the first image parameter information extraction module makes the image processing and parameter extraction process more efficient. After the image data is clarified, the relevant parameter information is extracted immediately, providing fast and accurate data support for subsequent evaluation and adjustment, reducing unnecessary processing time and resource consumption. The formulas and parameters (such as Gy, Dy, Fy, etc.) in the technical solution can be adjusted according to actual application scenarios and needs to adapt to different types of images and different quality evaluation standards. This flexibility enables the system to be widely used in various image processing fields to meet the personalized needs of different users. By introducing the concept of image optimization evaluation parameters and specific calculation methods, new ideas and methods are provided for the research of image processing technology. This innovative technical solution helps to promote technological progress and development in the field of image processing and provide more advanced and efficient solutions for applications in related fields.

[0091] Specifically, the evaluation and adjustment module includes:

[0092] A comparison module, used for comparing the first image optimization evaluation parameter with a preset image optimization evaluation parameter threshold;

[0093] An image optimization degree determination module, configured to determine that the image optimization degree is poor when the first image optimization evaluation parameter is lower than a preset image optimization evaluation parameter threshold;

[0094] A second image parameter information extraction module is used to retrieve the image parameter information of the image data before the sharpening process as the second image parameter information when the optimization degree of the determined image is poor; wherein the second image parameter information includes the average gradient, contrast value and resolution value of the image data before the sharpening process;

[0095] The image adjustment degree coefficient acquisition module is used to obtain the image adjustment degree coefficient by using the first image parameter information and the second image parameter information; wherein the image adjustment degree coefficient is obtained by the following formula:

[0096]

[0097] Where k represents the image adjustment coefficient; G a01 , D a01 and F a01 represents the average gradient, contrast value and resolution value included in the first image parameter information; G a02 , D a02 and F a02 Indicates the average gradient, contrast value and resolution value included in the second image parameter information;

[0098] A minimum standard value acquisition module is used to retrieve the minimum average gradient, contrast value and resolution value allowed under the condition of meeting the image clarity;

[0099] The standard value adjustment module is used to adjust the minimum average gradient, contrast value and resolution value allowed under the condition of satisfying the image clarity by using the image adjustment degree coefficient, and obtain the adjusted minimum average gradient, contrast value and resolution value; wherein the adjusted minimum average gradient, contrast value and resolution value are obtained by the following formula:

[0100]

[0101] Among them, G yt Indicates the minimum average gradient after adjustment; k is the image adjustment coefficient; G y Indicates the minimum average gradient allowed under the condition of satisfying image clarity; G a01 represents the average gradient contained in the first image parameter information; G a02 represents the average gradient included in the second image parameter information;

[0102]

[0103] Among them, D yt represents the minimum average gradient after adjustment; k is the image adjustment coefficient; D yIndicates the minimum contrast value allowed under the condition of meeting the image clarity; D a0D1 represents the contrast value included in the first image parameter information; D a02 Indicates the contrast value included in the second image parameter information;

[0104]

[0105] Among them, F yt Indicates the minimum resolution value after adjustment; k is the image adjustment coefficient; F y Indicates the minimum resolution value allowed under the condition of meeting the image clarity; F a0D1 Indicates the resolution value included in the first image parameter information; F a02 Indicates the resolution value included in the second image parameter information;

[0106] The image data secondary sharpening processing module is used to perform secondary sharpening processing on the image data based on the adjusted minimum average gradient, contrast value and resolution value as a standard until the average gradient, contrast value and resolution value of the image data reach or exceed the adjusted minimum average gradient, contrast value and resolution value.

[0107] The technical effect of the above technical solution is: by comparing the first image optimization evaluation parameter with the preset threshold through the comparison module, the optimization degree of the image can be accurately evaluated. When the evaluation result shows that the image optimization degree is poor, the system automatically triggers a further optimization process to ensure that the image quality is continuously improved. The introduction of the image adjustment degree coefficient (k) enables the system to dynamically adjust the minimum average gradient, contrast value and resolution value that meet the image clarity according to the difference in image parameter information before and after the sharpening process. This dynamic adjustment mechanism helps to guide the subsequent image processing process more accurately and improve processing efficiency and effect.

[0108] When it is determined that the image optimization degree is poor, the system can automatically retrieve the image data before the sharpening process, and perform a secondary sharpening process on the image based on the adjusted minimum average gradient, contrast value and resolution value. This processing method ensures that the image quality can be further improved when it does not meet the expected standards, thereby meeting higher image quality requirements. The design of the entire evaluation and adjustment module is highly automated, and no manual intervention is required from evaluation to adjustment to secondary processing. This not only improves the efficiency of image processing, but also reduces the errors that may be caused by manual operation, ensuring the stability and consistency of image quality. The parameters and formulas in the technical solution can be adjusted according to the actual application scenarios and requirements to adapt to different types of images and different quality requirements. This flexibility enables the system to be widely used in various image processing fields to meet the personalized needs of different users. By introducing the concepts of image adjustment degree coefficient and dynamic adjustment optimization standard, new ideas and methods are provided for the research of image processing technology. This innovative technical solution helps to promote technological progress and development in the field of image processing and provide more advanced and efficient solutions for applications in related fields.

[0109] In summary, this technical solution has demonstrated significant technical effects in improving the accuracy of image quality evaluation, realizing intelligent image optimization, improving image processing efficiency and automation, enhancing system adaptability, and promoting technological innovation and development.

[0110] Operation and maintenance management system, including:

[0111] The personnel management module is used for self-registration of workers, real-name attendance management and personnel file management through the labor real-name management system. The labor real-name management system includes dual-platform control of mobile phones and WEB terminals. Real-name attendance management includes automatic recognition through face, fingerprint, documents, etc. The labor real-name management system realizes dual-platform control of mobile phones and WEB terminals, and automatically recognizes through face, fingerprint, documents, etc. It can quickly complete the verification of personnel, documents and health status and automatically generate attendance records, so as to perform a series of management functions such as attendance analysis, leave management, salary payment, labor business control, etc.

[0112] The mechanical command module is connected to the construction machinery and equipment to collect and manage the location, fuel consumption data, operating status and trajectory information of the construction machinery and equipment in real time. By connecting with the construction machinery and equipment, it realizes the real-time collection and management of key information such as equipment location, fuel consumption, status and trajectory. This module focuses on monitoring the fuel consumption of the equipment and can provide real-time alarms and feedback on the operating status of the equipment. Using this data, it not only optimizes the fuel consumption management strategy of the equipment and improves construction efficiency, but also supports remote monitoring and control, helping managers make more accurate decisions based on data analysis.

[0113] The temporary power management module is used to monitor the safety status and energy consumption of temporary power use of construction machinery and equipment in real time. By real-time monitoring of the safety status and energy consumption of temporary power use, the safety and energy utilization efficiency of the construction site are effectively improved. Through remote monitoring and management, power equipment failures can be discovered and resolved in a timely manner, reducing the risk of safety accidents. At the same time, the power supply plan is optimized and the construction efficiency of the construction site is improved. At the same time, the collection and analysis of monitoring data also provides an important reference for green and low-carbon management.

[0114] The trestle speed management module is installed on the cameras around the trestle to monitor and manage the speed of vehicles in the trestle area in real time, record and analyze the monitored data, and generate corresponding reports based on the analysis results: This function is used to monitor and manage the speed of vehicles in the trestle area. It monitors the speed of vehicles in real time through sensors or cameras installed around the trestle, and records relevant data for subsequent analysis and management. When a vehicle exceeds the speed limit, the system can automatically trigger an alarm to remind relevant staff to take necessary measures to ensure the safety of the trestle area. In addition, by analyzing the data and generating reports, it can help managers understand the driving conditions of vehicles and formulate effective management measures.

[0115] The construction management module is used to refine the building information model using the work breakdown structure, connect the intelligent testing press, universal press and compression and flexure press to the Internet, and obtain real-time volume, mixing time, pan consumables details, mix ratio and theoretical concrete formula data of precast components.

[0116] The personnel safety management module is used to intelligently identify and send alarms for workers' wearing of safety helmets, flame monitoring, wearing of work clothes, and intrusion into restricted areas through surveillance cameras and AI intelligent algorithms. This helps to improve the level of construction site safety management, effectively prevent accidents, and ensure the safety of workers and on-site facilities.

[0117] Construction management module, including:

[0118] The progress management module is used to combine the Unreal Engine and the building information model, refine the building information model according to the work breakdown structure, link the planned progress with the actual progress, combine the progress and cost, and introduce overdue warning and delay correction functions to help managers understand the progress of the project more vividly and in detail, thereby improving the accuracy and controllability of progress management.

[0119] The smart experiment module is used to connect intelligent testing presses, universal machines and compression and flexure machines to the Internet, and to store, analyze, display and push test data, test calendars, test pass rates, test times and laboratory monitoring data. It can help managers to fully understand the laboratory's operating conditions and test data quality in real time, so as to adjust management strategies in time, improve experimental efficiency and provide reliable technical guidance for on-site construction.

[0120] The smart mixing module is connected to the mixing station to obtain the volume, mixing time, batch consumables details, mix ratio and theoretical formula data of concrete for precast components in real time. At the same time, it binds all concrete data with the on-site construction site, and can simultaneously analyze and display the latest batch data, raw material consumption data, production volume and proportion of each type of concrete, production volume in the past 15 days, early warning data and video monitoring of the mixing station on the WEB and UE sides, thereby helping managers to understand the operation status and production status of the mixing station in real time, improving the convenience and efficiency of management.

[0121] The jacking management module is used to build a digital model of jacking, simulate jacking, plan the construction path and design jacking parameters in advance according to the simulation results, and collect and analyze the geometric and physical parameter data of the structure and equipment in real time during the jacking process. According to the analysis results, the hydraulic control system is used to dynamically adjust the main beam jacking, jacking and lateral correction.

[0122] Security monitoring system, including:

[0123] The construction monitoring module is used to monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops, large-volume concrete temperature control and steel box cofferdam through intelligent strain sensors and intelligent temperature sensors: In order to ensure the safety of large temporary structure construction and improve the level of informatization of construction, ice pressure monitoring of the main bridge steel trestle and steel platform, horizontal and vertical displacement monitoring of pile tops, steel box cofferdam monitoring and large-volume concrete temperature monitoring are carried out, and the monitored data are analyzed. If there is deformation, the deformation monitoring results are uploaded to the platform. The intelligent strain sensors and intelligent temperature sensors are connected to the automatic collection and transmission equipment. By monitoring data changes in real time, the integration of data collection, processing, analysis, query and management and the visualization of monitoring results are realized.

[0124] The environmental monitoring module is used to monitor ice pressure and perform AI intelligent identification and monitoring of environmental loads.

[0125] Environmental monitoring module, including:

[0126] Monitor ice pressure: Use intelligent strain sensors to monitor the strain and deformation of the steel pipe surface during the icing process. Based on the research foundation of the mechanical properties of ice and steel pipes, the stress distribution on the steel pipe surface during the icing process is analyzed through strain analysis. Finite element analysis is used to analyze the impact of the icing process on the bearing performance of the steel pipe and evaluate the safety performance. The mechanical properties of ice are significantly affected by temperature, and the stress on the steel pipe surface shows nonlinear characteristics along the depth.

[0127] AI intelligent identification and monitoring of environmental loads: Real-time visual measurement of river surface velocity and waves, construction of a visual reconstruction algorithm for transient liquid surface of near-field waves on bridges, acquisition of three-dimensional liquid surface change process of waves in time and space according to wind speed, setting of structural risk warning values, simulation analysis of the mechanical state of steel trestles and steel box cofferdams, real-time monitoring of construction safety, early warning when the structural risk warning value is exceeded, and real-time acquisition of the bearing capacity and safety status of temporary structures on the river. With the goal of safe use of the structure, an evaluation system for mechanical responses such as stress distribution, displacement and deformation of the structure is established. By setting structural risk warning values, real-time construction safety monitoring and early warning are achieved, reducing work risks and improving construction quality.

[0128] Monitoring of carbon emissions in the construction environment: used to monitor carbon emissions according to the construction cycle, analyze the total carbon emissions, real-time carbon emissions, the proportion of carbon emissions in each part, the carbon emission trend of the project and the key information of green and low-carbon management of energy conservation and emission reduction. According to the analysis results, the uncertain carbon emissions in the construction process are managed in a refined manner and visualized through computer vision. The construction cycle includes the entire life cycle of material production, transportation, construction, etc.

[0129] Construction monitoring module, including:

[0130] Monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops: By arranging special prisms on the steel trestle and steel platform pile tops, a motor-driven total station is used to automatically find points and capture prisms to monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops, ensuring the stability of the steel trestle and steel platform and providing working space for on-site construction.

[0131] Monitor the temperature control of large-volume concrete: By selecting characteristic temperature measurement areas and points on the surface, inside and at different heights of the foundation, deploying temperature sensors, and connecting automated collection and transmission equipment, real-time monitoring of data changes can replace traditional manual monitoring, saving manpower and greatly improving efficiency. The temperature status of large-volume concrete can be grasped in time, providing strong support for on-site decision-making.

[0132] Monitoring of the steel box cofferdam: using the pre-buried inclinometer tube to monitor the deep horizontal displacement of the steel box, using the prism on the top of the box to monitor the horizontal and vertical displacement of the top of the steel box, and using the strain gauge to monitor the force changes of the internal support. Ensure that the steel box cofferdam is in good condition during operation, and provide a safe dry working environment for the construction of the bridge substructure.

[0133] Working principle: When using the intelligent construction engineering operation and maintenance management image information system of the present invention, according to Figure 1 and Figure 2 , including the following steps:

[0134] Step 1: Before construction, relevant data is collected through environmental sensors and image acquisition equipment, the data is processed, and a building information model is generated. The changes of day and night and the cycle of the four seasons are simulated on the building information model.

[0135] Step 2: During construction, workers are self-registered, and real-name attendance and personnel file management are carried out through the labor real-name management system. At the same time, intelligent identification and alarm push are carried out for workers' wearing of safety helmets, flame monitoring, work clothes wearing, and intrusion into restricted areas.

[0136] Step 3: Collect the location, fuel consumption data, operating status and trajectory information of construction machinery and equipment in real time, and focus on monitoring the fuel consumption of the equipment, the safety status of the equipment's temporary power use, and the energy consumption. When fuel consumption is abnormal, provide real-time alarm and feedback.

[0137] Step 4: The speed of vehicles in the pier area is monitored and managed in real time through cameras installed around the pier, and the monitored data is recorded and analyzed. A corresponding report is generated based on the analysis results. When a vehicle exceeds the speed limit, the system can automatically trigger an alarm to remind relevant staff to take necessary measures.

[0138] Step 5: During the construction process, monitor and analyze carbon emissions according to the construction cycle. Based on the analysis results, fine-tune the management of uncertain carbon emissions during the construction process and visualize them through computer vision.

[0139] Step 6: When icing occurs, analyze the impact of the icing process on the bearing performance of the steel pipe based on the surface strain and deformation of the steel pipe during the icing process, simulate and analyze the mechanical state of the steel trestle and steel box cofferdam, monitor the safety of the construction in real time, and issue an early warning when the structural risk warning value is exceeded.

[0140] In summary, the intelligent construction project operation and maintenance management image information system of the present invention effectively improves the safety and energy utilization efficiency of the construction site by real-time monitoring of the safety status and energy consumption of temporary electricity use. Through remote monitoring and management, it can timely discover and solve power equipment failures, reduce the risk of safety accidents, optimize the power supply plan, and improve the construction efficiency of the construction site. At the same time, the collection and analysis of monitoring data also provides an important reference for green and low-carbon management. By tracking and locating all greenhouse gases generated directly or indirectly during the construction period, classifying and analyzing the total carbon emissions during the construction period in stages, the uncertain carbon emissions during the construction process are finely managed, and strict Control the energy consumption and material loss in each construction link, optimize the construction management from the perspective of green and low-carbon construction while completing energy conservation and emission reduction, realize the comprehensive coordination and optimization of the construction management of this project, realize the real-time acquisition of the bearing capacity and safety status of the temporary structure on the river, and establish an evaluation system for the mechanical response of the structure such as stress distribution, displacement and deformation with the goal of safe use of the structure. By setting the structural risk warning value, real-time construction safety monitoring and early warning can be achieved, thus reducing work risks and improving construction quality. By binding all concrete data with the on-site construction site, it can help managers understand the operation status and production status of the mixing plant in real time, thus improving the convenience and efficiency of management.

[0141] It should be noted that, in the invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0142] Example:

[0143] The intelligent construction engineering operation and maintenance management image information system proposed in this invention has been applied in the construction process of the Songhua River Bridge. Figure 3 As shown (this does not constitute a limitation to the protection of the present invention).

[0144] The digital twin platform of the smart construction site of the Songhua River Bridge integrates a variety of advanced information technologies, and the present invention is part of the technology of the platform. It uses technologies such as Unreal Engine (UE), Internet of Things (IoT), Building Information Model (BIM), Artificial Intelligence (AI) and Computer Vision (CV) to provide comprehensive digital support and management for the construction process. Through various sensors and image acquisition equipment, the platform comprehensively captures various data during the construction of the Songhua River Bridge, including multi-dimensional information such as the main structure, surrounding environment, temporary building facilities, meteorological conditions and river water dynamics. It not only simulates the changes of day and night and the cycle of the four seasons, but also interacts with the real world in real time, thereby realizing a comprehensive perception of the four-dimensional environment of "air-sky-ground-water". In this way, the platform constructs a real-time dynamic construction model that accurately reflects the actual construction site of the Songhua River Bridge project.

[0145] The platform includes basic modules such as personnel management, mechanical equipment management, temporary power management, and video surveillance management. At the same time, it focuses on the key and difficult points of the project, scientific research highlights, and key control contents of the construction to develop four functional modules: intelligent construction and equipment, safe and resilient construction, green and low-carbon construction, and lean quality construction.

[0146] 1. Basic Module

[0147] 1. Personnel management module: The real-name labor system is used to realize dual-platform control on mobile phones and web terminals. The personnel management module mainly has functions such as mobile terminal worker self-registration, real-name attendance management, and personnel file management. In real-name attendance management, it supports automatic recognition through face, fingerprint, and ID, quickly completes the verification of personnel, ID, and health status, and automatically generates attendance records, so as to perform a series of management functions such as attendance analysis, leave management, salary payment, and labor business control.

[0148] 2. Mechanical Commander: By connecting with various types of construction machinery and equipment, it realizes the real-time collection and management of key information such as equipment location, fuel consumption, status and trajectory. This module focuses on monitoring the fuel consumption of the equipment and can provide real-time alarms and feedback on the operating status of the equipment. Using this data, the platform not only optimizes the fuel consumption management strategy of the equipment and improves construction efficiency, but also supports remote monitoring and control, helping managers make more accurate decisions based on data analysis.

[0149] 3. Temporary power monitoring and management: By real-time monitoring of the safety status and energy consumption of temporary power, the safety and energy efficiency of the construction site are effectively improved. Through remote monitoring and management, the system can promptly detect and resolve power equipment failures, reduce the risk of safety accidents, optimize power supply plans, and improve construction efficiency on the construction site. At the same time, the collection and analysis of monitoring data also provides an important reference for green and low-carbon management.

[0150] 4. Video surveillance and AI intelligent identification system for safe behavior: The system installs on-site surveillance cameras and AI intelligent algorithms to realize intelligent identification and alarm push for workers' wearing of safety helmets, flame monitoring, wearing of work clothes, and entering restricted areas. It helps to improve the level of construction site safety management, effectively prevent accidents, and ensure the safety of workers and on-site facilities.

[0151] 5. Trestle speed management: This function is used to monitor and manage the speed of vehicles in the trestle area. It monitors the speed of vehicles in real time through sensors or cameras installed around the trestle, and records relevant data for subsequent analysis and management. When a vehicle exceeds the speed limit, the system can automatically trigger an alarm to remind relevant staff to take necessary measures to ensure the safety of the trestle area. In addition, the system can also perform data analysis and generate reports or charts to help managers understand vehicle driving conditions and formulate effective management measures.

[0152] 2. Security and Resilience Construction

[0153] 1. Safety monitoring of large temporary structure construction: To ensure the safety of large temporary structure construction and improve the level of information technology of construction, we carried out monitoring work such as ice pressure monitoring of the steel trestle and steel platform of the main bridge, horizontal displacement and vertical displacement monitoring of pile tops, steel cofferdam monitoring, and temperature monitoring of large-volume concrete. Deformation monitoring results are uploaded to the platform every day; strain and temperature sensors are connected to automatic collection and transmission equipment, and data changes are monitored in real time through the cloud network platform, realizing the integration of data collection, processing, analysis, query and management, and visualization of monitoring results.

[0154] (1) Monitoring of horizontal and vertical displacement of steel trestle and steel platform pile tops

[0155] By arranging special prisms on the top of the steel trestle and steel platform piles, using a motor-driven total station to automatically find points to capture the prisms, the horizontal and vertical displacements of the tops of the steel trestle and steel platform piles are monitored to ensure the stability of the steel trestle and steel platform and provide working space for on-site construction.

[0156] (2) Temperature control monitoring of large volume concrete

[0157] By selecting characteristic temperature measurement areas and points on the surface, inside and at different heights of the foundation, deploying temperature sensors, connecting automated collection and transmission equipment, and monitoring data changes in real time through a cloud network platform, traditional manual monitoring can be replaced, saving manpower, greatly improving efficiency, and timely grasping the temperature status of large-volume concrete, providing strong support for on-site decision-making.

[0158] (3) Monitoring of steel box steel cofferdam, monitoring overview:

[0159] Through the pre-buried inclinometer, the horizontal displacement of the deep layer of the casing is monitored by an inclinometer; the horizontal and vertical displacement of the top of the casing is monitored through the prism on the top of the casing; and the stress change of the internal support is monitored through the strain gauge. This ensures that the steel cofferdam is in good condition during operation and provides a safe dry working environment for the construction of the bridge substructure.

[0160] 2. Environmental load

[0161] (1) Ice pressure monitoring

[0162] Intelligent strain sensors are used to monitor the surface strain and deformation of steel pipes during icing. Based on the research foundation of ice and steel pipe mechanical properties, strain analysis is used to study the stress distribution on the surface of steel pipes during icing. Based on the test results, finite element analysis is used to perform parameter analysis, study the impact of icing on the bearing performance of steel pipes, and evaluate safety performance.

[0163] The mechanical properties of ice are significantly affected by temperature, and the surface stress of the steel pipe shows nonlinear characteristics along the depth. Intelligent temperature sensors are used to monitor the relationship between temperature and depth.

[0164] (2) AI intelligent identification and monitoring of environmental loads: This module has developed a visual measurement system for the velocity and waves of the Songhua River. Based on this system, a visual reconstruction algorithm for the transient liquid surface of near-field waves on bridges has been constructed, which can obtain the three-dimensional liquid surface change process of waves in time and space according to the wind speed. On this basis, by simulating and analyzing the mechanical state of temporary structures on the river such as steel trestles and steel cofferdams, the real-time acquisition of the bearing capacity and safety status of temporary structures on the river is achieved. With the goal of safe use of the structure, an evaluation system for mechanical responses such as structural stress distribution, displacement and deformation is established, and structural risk warning values ​​are set to achieve real-time construction safety monitoring and early warning, reduce work risks and improve construction quality.

[0165] 3. Green and Low-Carbon Construction

[0166] This project has realized the first application of green and low-carbon construction management of cold region infrastructure. The project collects data on green and low-carbon construction through a variety of collection methods such as environmental sensors, intelligent mechanical commanders, and intelligent power sensors. Then, based on the carbon footprint theory, it plans to use the input-output method to establish an economic input-output-life cycle assessment model to track and locate all greenhouse gases directly or indirectly generated by the project throughout its life cycle, including material production, transportation, and construction.

[0167] The platform can analyze and display the total carbon emissions of the project by classification and stage, real-time carbon emissions, carbon emissions proportion of each part, carbon emissions trend of the project, energy conservation and emission reduction effects and other key information of green and low-carbon management. Refined management is carried out for the uncertain carbon emissions during the construction process, and energy consumption and material loss in each construction link are strictly controlled through pre-, mid- and post-process management methods and construction process improvements. While completing energy conservation and emission reduction, construction management is optimized from the perspective of green and low-carbon construction, realizing comprehensive coordination and optimization of construction management of this project.

[0168] 4. Lean Quality Construction

[0169] 1. Progress management module: This module combines the project BIM model and UE model, refines the model according to the work breakdown structure (WBS), links the planned progress with the actual progress, combines the progress with the cost, and introduces overdue warning and delay correction functions to help managers understand the project progress more vividly and in detail, thereby improving the accuracy and controllability of progress management.

[0170] 2. Smart laboratory system: This module connects intelligent testing presses, universal machines, compression and flexure machines and other equipment to the Internet, and stores, analyzes, displays and pushes test data, test calendars, test pass rates, test times, laboratory monitoring and other data, helping managers to fully understand the laboratory's operating conditions and test data quality in real time, so as to adjust management strategies in a timely manner, improve experimental efficiency, and provide reliable technical guidance for on-site construction.

[0171] 3. Smart mixing station system: This module is connected to the mixing station and can obtain real-time mixing plate information such as volume, mixing time, plate consumables details and mix ratio, as well as concrete theoretical formula data of prefabricated components, and bind all concrete data to the on-site construction site. It can simultaneously analyze and display the latest plate data, raw material consumption data, production volume and proportion of each type of concrete, production volume in the past 15 days, early warning data, and mixing station video monitoring data on the WEB and UE sides, so as to help managers understand the operation status and production status of the mixing station in real time, and improve the convenience and efficiency of management.

[0172] 5. Intelligent construction and equipment

[0173] In terms of intelligent equipment, we have integrated artificial intelligence and Internet of Things information technology to realize the intelligent construction of the main structure of the self-anchored suspension bridge based on the steel box girder jacking device, main tower hydraulic climbing formwork and main cable control device. By building a digital jacking model, we simulate the jacking, plan the construction path and design the jacking parameters in advance. During the jacking process, we collect and analyze the geometric and physical parameter data of the structure and equipment in real time, and realize the dynamic adjustment of the main beam jacking, jacking and lateral correction through the hydraulic control system.

[0174] In summary, although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, which are all within the scope of protection of the present invention.

Claims

1. An intelligent construction engineering operation and maintenance management image information system, characterized by: include: Data collection system, used to collect environmental data, meteorological data, multi-dimensional information data of river water dynamics in the construction area, and image data of the main structure and temporary building facilities under construction, and generate a building information model based on the collected data; The operation and maintenance management system is used to manage personnel, construction machinery and equipment, temporary power safety status, trestle speed measurement, construction structure and personnel safety, and simulate and adjust the jacking; The safety monitoring system is used to monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops, temperature control of large-volume concrete, steel box cofferdams and ice pressure, and perform AI intelligent identification and monitoring of environmental loads.

2. According to claim 1, the intelligent construction engineering operation and maintenance management image information system is characterized by: The data acquisition system comprises: Environmental sensors are used to collect environmental data, meteorological data and multi-dimensional information data of the river water dynamics in the construction area. The sensors include temperature and humidity sensors, meteorological sensors and flow rate sensors; Image acquisition equipment, used to collect real-time image data of the main structure and temporary construction facilities under construction. The temporary construction facilities include steel trestles and steel box cofferdams. The image acquisition equipment is a high-definition camera; The scene generation module is used to generate a building information model based on the data collected by the sensor and image acquisition devices.

3. The intelligent construction engineering operation and maintenance management image information system according to claim 2 is characterized by: The scene generation module comprises: A storage module is used to store the data collected by the sensor and the image acquisition device, and transmit the collected image data to the real scene processing module; The real scene processing module is used to perform a clear processing on the collected image data and transfer the processed image data to the building information model generation module; The building information model generation module is used to generate a building information model from the image data transmitted from the real scene acquisition module, and simulate the data collected by the sensor on the building information model.

4. The intelligent construction engineering operation and maintenance management image information system according to claim 3 is characterized by: Real scene processing module, including: An image data extraction module, used for extracting the image data after the image data is processed for clarity; A first image parameter information extraction module, used to extract the image parameter information of the image data that has completed the sharpening process as the first image parameter information; wherein the first image parameter information includes the average gradient, contrast value and resolution value of the image data that has completed the sharpening process; The first image optimization evaluation parameter acquisition module is used to obtain the first image optimization evaluation parameter using the first image parameter information; wherein the first image optimization evaluation parameter is obtained by the following formula: Where S represents the first image optimization evaluation parameter; G a01 , D a01 and F a01 represents the average gradient, contrast value and resolution value included in the first image parameter information; G y , D y and F y Indicates the minimum average gradient, contrast value and resolution value allowed under the condition of meeting the image clarity; H max and H min They represent the maximum and minimum grayscale values ​​corresponding to the pixels in the image data after the sharpening process; H z and H p They respectively represent the median and average values ​​of the grayscale values ​​corresponding to the pixels in the image data that has completed the sharpening process; An evaluation and adjustment module is used to evaluate and adjust the image quality of the image data that has completed the sharpening processing by using the first image optimization evaluation parameter.

5. The intelligent construction engineering operation and maintenance management image information system according to claim 4 is characterized by: Evaluation and adjustment module, including: A comparison module, used for comparing the first image optimization evaluation parameter with a preset image optimization evaluation parameter threshold; An image optimization degree determination module, configured to determine that the image optimization degree is poor when the first image optimization evaluation parameter is lower than a preset image optimization evaluation parameter threshold; A second image parameter information extraction module is used to retrieve the image parameter information of the image data before the sharpening process as the second image parameter information when the optimization degree of the determined image is poor; wherein the second image parameter information includes the average gradient, contrast value and resolution value of the image data before the sharpening process; The image adjustment degree coefficient acquisition module is used to obtain the image adjustment degree coefficient by using the first image parameter information and the second image parameter information; wherein the image adjustment degree coefficient is obtained by the following formula: Where k represents the image adjustment coefficient; G a01 , D a01 and F a01 represents the average gradient, contrast value and resolution value included in the first image parameter information; G a02 , D a02 and F a02 Indicates the average gradient, contrast value and resolution value included in the second image parameter information; A minimum standard value acquisition module is used to retrieve the minimum average gradient, contrast value and resolution value allowed under the condition of meeting the image clarity; The standard value adjustment module is used to adjust the minimum average gradient, contrast value and resolution value allowed under the condition of satisfying the image clarity by using the image adjustment degree coefficient, and obtain the adjusted minimum average gradient, contrast value and resolution value; wherein the adjusted minimum average gradient, contrast value and resolution value are obtained by the following formula: Among them, G yt Indicates the minimum average gradient after adjustment; k is the image adjustment coefficient; G y Indicates the minimum average gradient allowed under the condition of meeting the image clarity; G a01 represents the average gradient contained in the first image parameter information; G a02 represents the average gradient included in the second image parameter information; Among them, D yt represents the minimum average gradient after adjustment; k is the image adjustment coefficient; D y Indicates the minimum contrast value allowed under the condition of meeting the image clarity; D a0D1 represents the contrast value included in the first image parameter information; D a02 Indicates the contrast value included in the second image parameter information; Among them, F yt Indicates the minimum resolution value after adjustment; k is the image adjustment coefficient; F y Indicates the minimum resolution value allowed under the condition of meeting the image clarity; F a0D1 Indicates the resolution value included in the first image parameter information; F a02 Indicates the resolution value included in the second image parameter information; The image data secondary sharpening processing module is used to perform secondary sharpening processing on the image data based on the adjusted minimum average gradient, contrast value and resolution value as a standard until the average gradient, contrast value and resolution value of the image data reach or exceed the adjusted minimum average gradient, contrast value and resolution value.

6. The intelligent construction engineering operation and maintenance management image information system according to claim 1 is characterized by: The operation and maintenance management system comprises: The personnel management module is used to carry out worker self-registration, real-name attendance management and personnel file management through the labor real-name management system. The labor real-name management system includes dual-platform control on mobile phones and the web; The mechanical command module is connected to the construction machinery and equipment to collect and manage the location, fuel consumption data, operating status and trajectory information of the construction machinery and equipment in real time; Temporary power management module, used to monitor the safety status and energy consumption of temporary power consumption of construction machinery and equipment in real time; The trestle speed management module is installed on the cameras around the trestle to monitor and manage the speed of vehicles in the trestle area in real time, record and analyze the monitored data, and generate corresponding reports based on the analysis results; The construction management module is used to refine the building information model using the work breakdown structure, connect the intelligent test press, universal press and compression and flexure press to the network, and obtain the volume, mixing time, pan consumables details, mix ratio and theoretical concrete formula data of precast components in real time; The personnel safety management module is used to intelligently identify and send alarms for workers' wearing of safety helmets, flame monitoring, wearing of work clothes, and intrusion into restricted areas through surveillance cameras and AI intelligent algorithms.

7. The intelligent construction engineering operation and maintenance management image information system according to claim 6 is characterized by: The construction management module includes: A progress management module, which combines Unreal Engine with the building information model and refines the building information model according to the work breakdown structure; Smart experiment module, which is used to connect intelligent test presses, universal machines and compression and flexure machines to the Internet, and store, analyze, display and push test data, test calendar, test pass rate, test times and laboratory monitoring data; The smart mixing module is connected to the mixing station to obtain the volume, mixing time, pan consumables details, mix ratio and theoretical concrete formula data of precast components in real time, and bind all concrete data with the on-site construction site; The jacking management module is used to build a digital model of jacking, simulate jacking, plan the construction path and design jacking parameters in advance according to the simulation results, and collect and analyze the geometric and physical parameter data of the structure and equipment in real time during the jacking process. According to the analysis results, the hydraulic control system is used to dynamically adjust the main beam jacking, jacking and lateral correction.

8. The intelligent construction engineering operation and maintenance management image information system according to claim 1 is characterized by: The security monitoring system comprises: Construction monitoring module, which is used to monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops, mass concrete temperature control and steel box cofferdam through intelligent strain sensors and intelligent temperature sensors; The environmental monitoring module is used to monitor ice pressure and perform AI intelligent identification and monitoring of environmental loads.

9. The intelligent construction engineering operation and maintenance management image information system according to claim 8, characterized in that: The environmental monitoring module comprises: Monitoring ice pressure: Using intelligent strain sensors to monitor the strain and deformation of the steel pipe surface during the icing process, based on the research foundation of the mechanical properties of ice and steel pipes, the stress distribution on the steel pipe surface during the icing process is analyzed through strain analysis, and finite element analysis is used to analyze the impact of the icing process on the bearing performance of the steel pipe, and the safety performance is evaluated; AI intelligent identification and monitoring of environmental loads: Real-time measurement of river surface velocity and wave vision, construction of a bridge near-field wave transient liquid surface visual reconstruction algorithm, acquisition of the three-dimensional liquid surface change process of waves in time and space according to the wind speed, setting of structural risk warning values, simulation analysis of the mechanical state of the steel trestle and steel box cofferdam, real-time monitoring of construction safety, and early warning when the structural risk warning value is exceeded; Monitoring of carbon emissions in the construction environment: used to monitor carbon emissions according to the construction cycle, analyze the total carbon emissions, real-time carbon emissions, the proportion of carbon emissions in each part, project carbon emissions trends and key information on green and low-carbon management of energy conservation and emission reduction effects, and conduct refined management of uncertain carbon emissions in the construction process based on the analysis results, and visualize them through computer vision.

10. The intelligent construction engineering operation and maintenance management image information system according to claim 8, characterized in that: The construction monitoring module comprises: Monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops: by arranging special prisms on the steel trestle and steel platform pile tops, using a motor-driven total station to automatically find points to capture the prisms, and monitor the horizontal and vertical displacement of the steel trestle and steel platform pile tops; Monitor the temperature control of large-volume concrete: select characteristic temperature measurement areas and points on the surface, inside and at different heights of the foundation, deploy temperature sensors, connect automatic collection and transmission equipment, and monitor data changes in real time; Monitoring of the steel casing cofferdam: using the pre-buried inclinometer tube to monitor the deep horizontal displacement of the steel casing, using the prism on the top of the casing to monitor the horizontal and vertical displacement of the top of the steel casing, and using the strain gauge to monitor the stress changes of the internal support.

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