Building engineering quality intelligent evaluation management method

Abstract

The invention discloses a building engineering quality intelligent evaluation management method, and relates to the technical field of quality supervision, and the method comprises the steps: S1, setting a recoverable multifunctional marker in each process construction region and process intersection region of a building construction site, and integrating a plurality of sensors in the marker; s2, using construction equipment and signal transmitting and receiving devices equipped on personnel; according to the method, data is accurately collected through a recyclable marker, transmission is guaranteed through a block chain, a causal inference model is accurately analyzed, problems are efficiently solved through a VR / AR and feedback mechanism, an environment-adaptive device and an edge computing center cope with network faults and knowledge graph accumulation experience, the limitation of an existing system is overcome, model parameters and rules can be accurately determined, and the reliability of the system is improved. Data are integrated, process interaction is captured, and accurate evaluation of multi-process cross quality is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of quality supervision, and in particular to an intelligent evaluation and management method for construction engineering quality. Background Art

[0002] The intelligent evaluation and management method for construction project quality refers to the use of modern information technology and intelligent means to collect, analyze and process data on various aspects of construction projects, such as the construction process, material quality, and structural safety of construction projects, and based on preset quality standards and models, to achieve objective, efficient, and accurate evaluation and scientific management of construction project quality. It is a systematic method that aims to improve the level and effectiveness of construction project quality management, promptly discover quality problems, and take targeted measures to improve and optimize them.

[0003] In building construction, there are often multiple processes that are carried out in parallel. For example, during the construction of the main structure, concrete pouring, steel bar tying, formwork support and other processes are carried out simultaneously in different areas of the same floor, and each process will affect each other. For example, the vibration generated during concrete pouring may cause the position of the steel bars being tied in the adjacent area to shift, and the firmness of the formwork support may affect the flatness of the concrete after forming. However, it is very complicated to accurately identify and evaluate the quality of each process under the mutual interference of these cross-processes, because there are many interfering factors and they are interrelated. It is difficult to judge whether the quality meets the standards simply from the perspective of a single process.

[0004] In view of this, a construction project quality intelligent evaluation management method is provided to overcome the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a construction engineering quality intelligent evaluation management method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides a construction project quality intelligent evaluation management method, comprising the following steps: S1. Recyclable multifunctional markers are installed in the construction areas of each process and the cross-process areas of the construction site. Multiple sensors are integrated inside the markers, including but not limited to pressure sensors, acceleration sensors, angle sensors and micro-environmental sensors, and each marker has a unique electronic code; S2. Using the signal transmitting and receiving devices equipped on construction equipment and personnel, when the construction equipment or personnel are close to the marker at a certain distance, the sensor of the marker is automatically triggered to start collecting data, and the collected data is sent to the on-site data aggregation node through the wireless transmission module, and the data aggregation node then transmits the data to the cloud data processing platform; S3. In the cloud data processing platform, a process cross-quality evaluation system based on a causal inference model is established. The causal inference model analyzes a large amount of historical construction data and process data collected in real time under different working conditions to explore the potential causal relationship between the processes, and on this basis determines the weight coefficient of each process's impact on the overall quality under cross-interference; S4. Comprehensively evaluate the quality of each process through the quality evaluation system and generate a visual quality evaluation report. The report details the quality status of each process, potential risk points and the degree of mutual influence between different processes. At the same time, when the evaluation results are abnormal, the feedback adjustment mechanism is automatically activated to send instructions to relevant equipment and personnel at the construction site according to the pre-set adjustment strategy. The feedback adjustment mechanism combines the actual resource allocation and construction schedule of the construction site to dynamically optimize the priority and execution order of the adjustment instructions; S5. The recyclable multifunctional marker is made of a degradable and elastic material, and a texture structure is provided on the surface of the marker.

[0007] Furthermore, the signal transmitting and receiving device uses low-frequency radio waves for communication. When construction workers use devices with NFC function to approach the marker, they automatically read the electronic code and some basic information of the marker, including the working status of the sensor and the time of the last data collection. It also adopts an intermittent working mode, that is, when construction activities are detected around, the signal transmission and receiving functions are automatically turned on, and during the period of no construction activities, it enters a low-power standby state.

[0008] Furthermore, the wireless transmission module adopts a distributed data transmission protocol based on blockchain technology, packages the data collected by each marker into data blocks, and transmits and stores them through the blockchain network. During the data transmission process, each data block contains the hash value of the previous data block, and utilizes the distributed storage characteristics of the blockchain to back up the data on multiple nodes.

[0009] Furthermore, in the process of constructing the causal inference model, a method combining Bayesian network and deep learning is introduced. The Bayesian network is used to preliminarily model and infer the causal relationship between each process, determine the prior probability distribution between variables, and then use it as one of the input features of the deep learning model. Through the deep learning model, further feature extraction and model training of large-scale construction data are carried out to automatically learn the complex nonlinear relationship and potential causal laws between each process. In the model training process, the adversarial training method is adopted to introduce a generative adversarial network for adversarial training with the causal inference model. The generator of the adversarial network is used to generate false construction data, and the discriminator is used to distinguish between real data and false data generated by the generator.

[0010] Furthermore, the visual quality assessment report is displayed using virtual reality and augmented reality technologies. Construction workers and managers can wear corresponding VR / AR equipment to intuitively view the quality status of each process and the mutual influence between different processes at the construction site.

[0011] Furthermore, when sending instructions to the construction site, the feedback adjustment mechanism adopts a bio-inspired optimization algorithm to imitate the behavioral patterns of biological groups in nature, including but not limited to one of the ant colony algorithm and particle swarm optimization algorithm. The equipment and personnel at the construction site are regarded as a biological group, each individual has different attributes and behavioral capabilities, and the task allocation and action path of each individual are determined according to the quality assessment results and construction goals.

[0012] Furthermore, a temporary data processing center based on edge computing is set up at the construction site. When the network at the construction site fails or the delay is high, the data collected by the markers is first transmitted to the temporary data processing center for preliminary processing and analysis. The temporary data processing center uses a simplified causal inference model and quality evaluation algorithm to quickly process the data and generate a brief quality assessment report, which is promptly fed back to the management personnel at the construction site.

[0013] Furthermore, a construction experience knowledge base based on the knowledge graph will be established, which will organize and store construction techniques, quality issues, solutions and the relationships between various processes in historical building projects in the form of a graph. When conducting quality evaluation and problem solving, relevant knowledge and experience can be quickly acquired through the query and reasoning functions of the knowledge graph.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The recyclable multifunctional marker and its data triggering collection method solve the energy waste, data redundancy and accuracy problems of traditional data collection, and provide a reliable data basis for quality evaluation, so as to more accurately analyze the actual situation of each process under cross-interference, and overcome the difficulty of accurately evaluating the cross-quality of multiple processes due to data problems. In actual construction, accurate data can clearly show the impact of concrete pouring vibration on the steel bar binding position, making quality evaluation more targeted and accurate, and no longer limited to one-sided judgment of a single process.

[0015] The wireless transmission module based on blockchain technology ensures the security, stability and traceability of data transmission, effectively copes with the complex electromagnetic environment of the construction site, avoids data tampering and loss, and ensures the reliable source of quality evaluation data. In a complex construction environment, data can be transmitted to the processing platform completely and accurately, providing solid data support for subsequent quality analysis, and solving the problem of inaccurate or incomplete quality evaluation due to data transmission problems.

[0016] The evaluation system based on the causal inference model and combined with innovative methods such as Bayesian networks, deep learning and adversarial training has broken through the limitations of the traditional single-process evaluation model and can accurately analyze the complex causal relationship under the cross-interference of multiple processes and the weight of each process's impact on quality. Through the analysis and learning of a large amount of historical and real-time data, the model can adapt to changes in different construction scenarios and accurately judge the quality status of each process. It provides an effective model tool for solving the problem of cross-process quality evaluation and overcomes the problem that traditional methods are difficult to determine complex model parameters and evaluation rules.

[0017] The quality assessment report that combines visual VR / AR display with real-time communication functions, as well as the feedback adjustment mechanism based on the bio-inspired optimization algorithm, realizes the rapid discovery, efficient communication and precise resolution of quality problems. It not only optimizes the allocation of construction resources, improves construction efficiency and quality stability, but also effectively copes with the complexity and variability of quality problems under the cross-interference of multiple processes on the construction site. Through the intuitive display of quality problems by VR / AR equipment, construction personnel can quickly locate and communicate solutions with relevant personnel. The feedback adjustment mechanism optimizes resource allocation according to actual conditions, ensures smooth construction, avoids construction delays and rework caused by quality problems, and solves the current situation where existing technologies are difficult to quickly handle cross-process quality problems.

[0018] The signal transmitting and receiving device that communicates with low-frequency radio waves and integrates multiple functions adapts to the complex environment of the construction site, reduces power consumption and improves practicality and convenience, ensures the stable operation of data collection, further guarantees the effective implementation of the entire quality evaluation management system at the construction site, and solves the problem of unstable equipment communication and operation caused by the complex construction environment.

[0019] The edge computing temporary data processing center can ensure the continuation of quality evaluation work when there is a network failure or high latency, avoiding quality control loopholes caused by network problems. By using simplified models and algorithms for emergency handling and ensuring the integrity and continuity of data after the network is restored, the entire quality evaluation process is ensured to be free from excessive interference from network instability factors, solving the problem of the impact of unstable network on quality evaluation work at the construction site.

[0020] The knowledge graph-based construction experience knowledge base provides a rich knowledge reserve and decision-making support for quality evaluation and problem solving, which helps to improve the overall quality control level and continuous improvement capabilities of construction companies. By quickly querying and reasoning solutions to similar problems, and adjusting and optimizing them in accordance with actual conditions, historical experience is effectively used to solve quality problems in current construction, avoiding the recurrence of the same quality problems, solving the problem of the lack of effective integration and utilization of historical experience in existing technologies, and improving the level and effect of intelligent evaluation management of construction project quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a schematic diagram of a construction engineering quality intelligent evaluation management method. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0023] See also Figure 1 , the present invention provides a technical solution: See also Figure 1 As shown, an embodiment of a construction project quality intelligent evaluation management method: 1. Project Overview and Construction Scenario: In this embodiment, a large commercial complex construction project was selected. During the main structure construction phase, the construction area of ​​a certain floor was about 1,500 square meters, which was divided into four main construction areas, where processes such as concrete pouring, steel bar binding, formwork support, and tower crane lifting of construction materials were carried out respectively. The areas were adjacent to each other, and the cross-process operations were frequent and complicated.

[0024] 2. Arrangement and data collection of recyclable multifunctional markers: A total of 100 recyclable multifunctional markers were arranged at intervals of 15 square meters in each construction area and at key nodes where processes intersect. These markers integrate high-precision pressure sensors, acceleration sensors, angle sensors, and micro-environmental sensors that can monitor temperature, humidity, and dust concentration in real time. Each marker is given a unique electronic code to facilitate accurate identification and management of data.

[0025] For example, at the junction of the concrete pouring area and the steel bar binding area, the markers arranged can accurately sense the vibration intensity generated by concrete vibration (through the acceleration sensor, the accuracy can reach ±0.1m / s²), the small angle changes caused by external forces during the steel bar binding process (the angle sensor accuracy is ±0.5°), and the temperature and humidity of the surrounding environment (temperature accuracy ±0.5℃, humidity accuracy ±3%RH) and dust concentration (accuracy ±0.01mg / m³) and other parameters. These data are crucial for analyzing the mutual influence between working processes.

[0026] When construction equipment (such as concrete pump trucks, tower cranes lifting steel bars) or construction workers enter the 2.5-meter range around the marker, the signal transmitting and receiving devices equipped on the construction equipment and workers will automatically trigger the sensor of the marker to start collecting data. Compared with the traditional timed collection method, this distance-triggered data collection mechanism has significant advantages. In a concrete pouring operation, through this triggering method, the marker will only start data collection when the concrete pump truck approaches and performs pouring operations, avoiding the collection of invalid data during non-construction periods. It not only reduces the pressure of data transmission and storage, but also ensures that the collected data is closely related to the actual construction activities, providing highly accurate and targeted basic data for subsequent quality evaluation, and effectively solving the problem of inaccurate quality evaluation caused by data redundancy and irrelevant data interference.

[0027] 3. Data transmission and storage: The data collected by the markers are sent to the on-site data aggregation node through the wireless transmission module. The wireless transmission module uses a distributed data transmission protocol based on blockchain technology to package the data collected by each marker into data blocks (the data block size is 1MB, containing the collected data within 10 minutes), and transmit and store them through the blockchain network.

[0028] There are many large electrical equipment and metal structures at the construction site, such as welding machines, cranes, etc. These devices will generate strong electromagnetic interference during operation, which seriously affects the stability and accuracy of traditional wireless transmission data. The distributed data transmission protocol of blockchain technology ensures the security, immutability and traceability of data through encryption algorithms and hash functions. For example, in a strong electromagnetic interference event, a large number of packets were lost and errors occurred in the traditional wireless transmission data, but the data blocks transmitted based on blockchain technology, due to its unique chain structure and encryption mechanism, each data block contains the hash value of the previous data block, forming a complete data chain. Once a data block is tampered with, the subsequent data blocks will not be able to be verified, thereby ensuring the integrity and reliability of the data, so that the quality data of the construction site can be stably and securely transmitted to the on-site data aggregation node, and then transmitted to the cloud data processing platform, avoiding the inaccurate or incomplete quality evaluation caused by data transmission problems, and providing reliable data support for the subsequent cross-process quality evaluation.

[0029] The on-site data aggregation node then transmits the data to the cloud data processing platform. During the transmission process, even if some network nodes fail, the distributed storage characteristics of the blockchain can ensure that the data is backed up on multiple nodes (5 backup nodes are set up in this project, distributed in different geographical locations), which will not cause data loss, ensuring the continuous availability of data and providing reliable data support for subsequent cross-process quality evaluation.

[0030] 4. Process cross-quality evaluation system based on causal inference model: In the cloud data processing platform, a process cross-quality evaluation system based on the causal inference model is established. The model analyzes a large amount of historical construction data (including data accumulated in the early construction stage of this project and historical data of similar construction projects, totaling more than 100 projects) and process data collected in real time under different working conditions, explores the potential causal relationship between the processes, and on this basis determines the weight coefficient of each process's impact on the overall quality under cross-interference.

[0031] For example, through in-depth analysis of historical data, it was found that under specific concrete mix ratios and pouring speeds, when the vibration acceleration during concrete pouring exceeds 0.5m / s² and lasts for more than 30 seconds, the probability that the position of steel bars with a diameter of 16mm or less being tied in the adjacent area will shift is as high as 80%, thereby determining the causal relationship and corresponding weight coefficient of the impact of concrete pouring vibration on the quality of steel bar tying of specific specifications; at the same time, combined with real-time collected data, such as vibration data in the current concrete pouring area and steel bar position change data fed back by markers in the surrounding steel bar tying area, this causal relationship model is continuously optimized and adjusted.

[0032] In the process of model construction, a method combining Bayesian network and deep learning was introduced. The Bayesian network was used to preliminarily model and infer the causal relationship between each process, determine the prior probability distribution between variables (for example, determine the prior probability relationship between the vibration intensity of concrete pouring and the position offset of the steel bar), and then use it as one of the input features of the deep learning model. Through the deep learning model, large-scale construction data is further extracted and model trained to automatically learn the complex nonlinear relationship and potential causal law between each process. In addition, in the process of model training, the adversarial training method was adopted, and a generative adversarial network (GAN) was introduced to conduct adversarial training with the causal inference model. The generator of GAN is used to generate false construction data (such as simulated steel deformation data that does not conform to the actual physical law), and the discriminator is used to distinguish between real data and false data generated by the generator. Through this adversarial training mechanism, the robustness and generalization ability of the causal inference model are continuously improved, so that it can adapt to the multi-process cross-quality evaluation needs of different construction projects and different construction environments.

[0033] For example, when faced with changes in the construction environment due to weather changes (such as sudden wind with wind speeds reaching 8m / s) at the construction site, the model can still accurately analyze the mutual influence relationship between the various processes, and the quality evaluation of processes such as steel bar binding, formwork support and concrete pouring is not affected by environmental factors. It can accurately judge the quality status of each process, overcoming the limitations of traditional single-process evaluation models that cannot consider the cross-interference of multiple processes and the influence of environmental factors, providing a more comprehensive and accurate basis for the quality evaluation of the construction site, and effectively solving the problem that quality evaluation models are difficult to accurately construct and apply under the cross-interference of multiple processes.

[0034] 5. Visual quality assessment report and problem feedback adjustment: The quality evaluation system mentioned above can be used to comprehensively evaluate the quality of each process and generate a visual quality evaluation report. Construction workers and managers can wear corresponding VR / AR equipment (equipment weight does not exceed 500 grams, with a display screen with 1080P resolution and 120Hz refresh rate, a viewing angle of 100°, and support for 5G network communication) to intuitively view the quality status of each process and the mutual influence between different processes at the construction site.

[0035] In the virtual scene, the quality level of each process is displayed in different colors (such as green for qualified quality, yellow for potential risks, and red for unqualified quality) and brightness. Dynamic lines and arrows (the thickness of the lines changes dynamically according to the degree of impact, and the direction of the arrows indicates the direction of impact) are used to indicate the direction and intensity of interaction between the processes. In addition, gestures (such as sliding, zooming, rotating, etc.) or voice commands (such as "view quality details of the concrete pouring area") can be used to zoom, rotate, and translate in the virtual scene to view quality details in different areas and angles.

[0036] For example, when the concrete pouring quality level in a certain area is found to be yellow, it is found through VR / AR equipment that the uneven pressure in some areas near the formwork support causes the flatness of the concrete after molding to not meet the requirements, and the interaction between the formwork and the concrete pouring process can be clearly seen through the visual display. At the same time, the VR / AR equipment integrates real-time communication functions (supporting high-definition video calls with a delay of no more than 50ms). On-site personnel can directly use the equipment to make video calls or voice communications with other relevant personnel (such as the person in charge of the formwork construction team and the concrete pouring technician) to discuss solutions together, which improves communication efficiency and the timeliness of problem solving, and avoids delays in handling quality problems due to poor communication.

[0037] When the evaluation results are abnormal, the feedback adjustment mechanism is automatically activated to send instructions to the relevant equipment and personnel at the construction site according to the pre-set adjustment strategy to correct the quality problems in the construction process in real time. The feedback adjustment mechanism combines the actual resource allocation of the construction site (such as the number of personnel, skill level of each construction team, type, quantity, distribution and availability of construction equipment and other detailed information) and the construction schedule (accurate to the planned completion time and current actual progress of each process, with an error of no more than 10 minutes), dynamically optimizes the priority and execution order of the adjustment instructions to ensure the efficiency and quality stability of the construction process.

[0038] For example, when it is found that there are many problems with the position deviation of steel bars in the steel bar binding process due to the vibration of concrete pouring, the feedback adjustment mechanism will give priority to arranging the steel bar workers who are closest to the area and have rich experience (through the construction personnel positioning system, the accuracy can reach ±1 meter) to make adjustments according to the on-site conditions. At the same time, the tower crane is dispatched to prioritize the steel reinforcement materials needed in the area (according to the material inventory management system, to ensure the timely supply of materials), and according to the overall construction progress, the speed and vibration method of subsequent concrete pouring are reasonably arranged (by adjusting the pumping pressure of the concrete pump truck and the vibration frequency of the vibrating rod) to avoid secondary impact on the adjusted steel bars, ensuring that the entire construction process can be efficiently promoted under the premise of ensuring quality, effectively solving the problem of timely discovery and precise handling of quality problems under the cross-interference of multiple processes, avoiding construction delays and rework caused by quality problems, and improving construction efficiency and project quality.

[0039] 6. Optimization of signal transmitting and receiving devices: The signal transmitting and receiving devices equipped on construction equipment and personnel use low-frequency (433MHz) radio waves for communication. The signals have strong penetration and anti-interference capabilities and can work stably in the complex electromagnetic environment of the construction site. For example, when there are a large number of metal structures (such as tower cranes, scaffolding, etc.) and electrical equipment on the construction site, low-frequency radio waves can still reliably communicate with the markers, ensuring the timeliness and accuracy of data collection and effectively reducing the risk of data loss due to communication failures.

[0040] The device also integrates the near-field communication (NFC) function. When construction workers use NFC-enabled devices (such as smartphones) to approach the marker, they can automatically read the electronic code and some basic information of the marker, such as the working status of the sensor (whether it is working properly, whether there is a fault code), the time of the last data collection (accurate to seconds), etc., which is convenient for construction workers to quickly inspect and troubleshoot the marker. During a routine inspection, the construction workers quickly discovered through the NFC function that the acceleration sensor data of a marker had not been updated for a long time, and promptly checked and replaced the battery of the marker, avoiding data loss due to sensor failure, ensuring the stable operation of the quality evaluation system, and improving the efficiency and convenience of equipment maintenance on the construction site.

[0041] At the same time, in order to reduce the power consumption of the device, an intermittent working mode is adopted, that is, when construction activities are detected around (through the coordinated judgment of the built-in sound sensor and motion sensor, the accuracy rate reaches more than 95%), the signal transmission and reception functions are automatically turned on, and during the period of no construction activities, it enters a low-power standby state to extend the battery life (in the intermittent working mode, the battery life is extended by more than 50% compared with the continuous working mode). In addition, a solar charging panel (the conversion efficiency of the solar charging panel reaches more than 20%) is set on the outer shell of the device to charge the device using natural light at the construction site, further improving the energy self-sufficiency of the device and reducing dependence on external power sources. The solar charging panel adopts a foldable design and can be folded and stored when not in use to avoid damage during the construction process, reducing the maintenance cost of the device and dependence on external energy, and improving the practicality and sustainability of the entire system in the complex environment of the construction site.

[0042] 7. The role of edge computing temporary data processing center: A temporary data processing center based on edge computing is set up at the construction site. When the network at the construction site fails or has a high delay (network delay exceeds 200ms), the data collected by the markers is first transmitted to the temporary data processing center for preliminary processing and analysis. The temporary data processing center uses a simplified causal inference model and quality evaluation algorithm (the computational complexity of the simplified model is 60% lower than the full cloud version, but it can still ensure that the accuracy of the preliminary judgment of key quality issues reaches more than 80%), which can quickly process the data and generate a brief quality assessment report, which is promptly fed back to the management personnel at the construction site so that they can take some necessary emergency measures before the network returns to normal to ensure that the construction quality is not greatly affected.

[0043] For example, during a network failure, the temporary data processing center initially determined that the concrete pouring in a certain area might have a problem of loose vibration based on the data collected from several surrounding markers, and promptly notified the concrete pouring team to conduct manual inspections and local vibration reinforcement, thus avoiding further deterioration of quality problems caused by network problems. At the same time, the temporary data processing center also has a data cache function (the cache capacity is 10GB, which can cache all marker data within 2 hours), caches the collected data, and when the network is restored, transmits the cached data to the cloud data processing platform for further analysis and processing to ensure the integrity and continuity of the data, and ensures the reliability and effectiveness of the entire quality evaluation system under unstable network conditions, providing a strong guarantee for quality control at the construction site, and effectively responding to the impact of unstable network at the construction site on quality evaluation work.

[0044] 8. Application of construction experience knowledge base based on knowledge graph: In the entire intelligent evaluation management system, a construction experience knowledge base based on the knowledge graph is established to organize and store knowledge such as construction technology, quality issues, solutions, and the relationships between various processes in historical building projects in the form of a graph.

[0045] For example, when surface cracks appeared in the concrete pouring process of this project, the query and reasoning function of the knowledge graph (the query response time does not exceed 3 seconds) can quickly obtain the reasons in previous similar projects, which may be due to improper concrete mix ratio (such as excessive cement dosage, unreasonable sand and gravel grading), untimely curing after pouring (curing time is less than the prescribed 7 days) or premature formwork removal (removal time is more than 2 days earlier than the standard). Combined with the actual situation of the current project, such as the source of concrete raw materials (through the raw material traceability system, the manufacturer, batch and performance indicators of cement are clarified), the current weather conditions (temperature 30℃, humidity 40%) and the formwork removal plan (the original planned removal time was 5 days after pouring), etc., appropriate adjustments and optimizations are made, such as adjusting the concrete mix parameters (reducing cement dosage by 10%, optimizing sand and gravel grading), strengthening curing measures after pouring (increasing the number of curing times and extending the curing time to 10 days) and reasonably postponing the formwork removal time (postponing it to 8 days after pouring), etc., which effectively solves the problem of concrete surface cracks, avoids the recurrence of quality problems, and improves the quality of the project.

[0046] At the same time, by using the semantic understanding and association analysis capabilities of the knowledge graph, it is possible to automatically discover potential connections and common problems between different construction projects, and provide construction companies with directions for quality improvement and management optimization. For example, through the analysis of the knowledge graphs of multiple projects, it was found that some construction teams are prone to common quality problems in the steel bar binding process (such as uneven steel bar spacing, loose binding wires, etc.). The company can provide targeted special training and technical guidance to these teams (the training content includes standard operating procedures for steel bar binding, quality inspection methods, etc.), improve the overall technical level and quality awareness of the construction team, further improve the quality control level of construction projects, effectively use historical experience knowledge to solve quality problems in current construction, and provide support for the company's long-term quality improvement.

[0047] Summarize: It can be seen from the above embodiments that this "a method for intelligent evaluation and management of construction project quality" effectively solves the problem of quality evaluation caused by cross-interference of multiple processes during the construction process.

[0048] First of all, the recyclable multifunctional markers and their unique data triggering and collection method provide an accurate and efficient means of data collection, solving the problems of energy waste, data redundancy and lack of accuracy in traditional data collection methods. At the same time, their recyclable characteristics reduce costs and resource waste. The elasticity and texture design of the material extend the service life and ensure the stability of data collection, providing a reliable data basis for subsequent quality evaluation.

[0049] Secondly, the wireless transmission module based on blockchain technology ensures the security, stability and traceability of data transmission, overcomes the interference of the complex electromagnetic environment of the construction site on data transmission and the problem of data tampering and loss that is prone to traditional transmission methods, and provides a reliable data source guarantee for quality evaluation.

[0050] Furthermore, the evaluation system based on the causal inference model, combined with innovative methods such as Bayesian networks, deep learning, and adversarial training, can accurately analyze the complex causal relationship under the cross-interference of multiple processes and the influence weight of each process on the quality, breaking through the limitations of the traditional single-process evaluation model, and realizing a comprehensive and accurate evaluation of the cross-process quality of construction projects, providing a strong basis for the diagnosis and resolution of quality problems in the construction process.

[0051] Then, the quality assessment report that combines visual VR / AR display with real-time communication function, and the feedback adjustment mechanism based on bio-inspired optimization algorithm, realizes the rapid discovery, efficient communication and precise resolution of quality problems, optimizes the allocation of construction resources, improves construction efficiency and quality stability, and effectively responds to the complexity and variability of quality problems under the cross-interference of multiple processes on the construction site.

[0052] In addition, the signal transmitting and receiving device that communicates with low-frequency radio waves and integrates multiple functions adapts to the construction site environment, reduces power consumption and improves the practicality and convenience of the device; the edge computing temporary data processing center ensures the continuation of quality evaluation work in the event of a network failure, avoiding quality control loopholes caused by network problems; the construction experience knowledge base based on the knowledge graph provides a rich knowledge reserve and decision-making support for quality evaluation and problem solving, which helps to improve the overall quality control level and continuous improvement capabilities of construction companies.

[0053] To sum up, this method comprehensively solves the problem of quality evaluation of multi-process cross-interference in construction through a series of innovative technical means and management mechanisms, improves the quality and construction efficiency of construction projects, has significant economic and social benefits, and provides a new and efficient solution for the field of construction project quality management. It effectively solves the problem of multi-process cross-quality evaluation that is difficult to solve with existing technologies, and improves the level and effect of intelligent evaluation management of construction project quality.

Claims

1. A construction engineering quality intelligent evaluation management method, characterized in that: The following steps are involved: S1. Recyclable multifunctional markers are installed in the construction areas of each process and the cross-process areas of the construction site. Multiple sensors are integrated inside the markers, including but not limited to pressure sensors, acceleration sensors, angle sensors and micro-environmental sensors, and each marker has a unique electronic code; S2. Using the signal transmitting and receiving devices equipped on construction equipment and personnel, when the construction equipment or personnel are close to the marker at a certain distance, the sensor of the marker is automatically triggered to start collecting data, and the collected data is sent to the on-site data aggregation node through the wireless transmission module, and the data aggregation node then transmits the data to the cloud data processing platform; S3. In the cloud data processing platform, a process cross-quality evaluation system based on a causal inference model is established. The causal inference model analyzes a large amount of historical construction data and process data collected in real time under different working conditions to explore the potential causal relationship between the processes, and on this basis determines the weight coefficient of each process's impact on the overall quality under cross-interference; S4. Comprehensively evaluate the quality of each process through the quality evaluation system and generate a visual quality evaluation report. The report details the quality status of each process, potential risk points and the degree of mutual influence between different processes. At the same time, when the evaluation results are abnormal, the feedback adjustment mechanism is automatically activated to send instructions to relevant equipment and personnel at the construction site according to the pre-set adjustment strategy. The feedback adjustment mechanism combines the actual resource allocation and construction schedule of the construction site to dynamically optimize the priority and execution order of the adjustment instructions; S5. The recyclable multifunctional marker is made of a degradable and elastic material, and a texture structure is provided on the surface of the marker.

2. A construction engineering quality intelligent evaluation management method as claimed in claim 1, characterized in that: The signal transmitting and receiving device uses low-frequency radio waves for communication. When construction workers use devices with NFC function to approach the marker, they automatically read the electronic code and some basic information of the marker, including the working status of the sensor and the time of the last data collection. It also adopts an intermittent working mode, that is, when construction activities are detected around, the signal transmission and receiving functions are automatically turned on, and during the period of no construction activities, it enters a low-power standby state.

3. A construction engineering quality intelligent evaluation management method as claimed in claim 1, characterized in that: The wireless transmission module adopts a distributed data transmission protocol based on blockchain technology, which packages the data collected by each marker into data blocks, and transmits and stores them through the blockchain network. During the data transmission process, each data block contains the hash value of the previous data block, and uses the distributed storage characteristics of the blockchain to back up the data on multiple nodes.

4. A construction engineering quality intelligent evaluation management method as claimed in claim 1, characterized in that: In the process of constructing the causal inference model, a method combining Bayesian network and deep learning is introduced. The Bayesian network is used to preliminarily model and infer the causal relationship between each process, determine the prior probability distribution between variables, and then use it as one of the input features of the deep learning model. Through the deep learning model, further feature extraction and model training of large-scale construction data are carried out to automatically learn the complex nonlinear relationship and potential causal laws between each process. In the model training process, the adversarial training method is adopted to introduce a generative adversarial network and the causal inference model for adversarial training. The generator of the adversarial network is used to generate false construction data, and the discriminator is used to distinguish between real data and false data generated by the generator.

5. The intelligent evaluation and management method for construction engineering quality according to claim 1, characterized in that: The visual quality assessment report is displayed using virtual reality and augmented reality technologies. Construction workers and managers can wear corresponding VR / AR equipment to intuitively view the quality status of each process and the mutual influence between different processes at the construction site.

6. A construction engineering quality intelligent evaluation management method as claimed in claim 1, characterized in that: When sending instructions to the construction site, the feedback adjustment mechanism adopts a bio-inspired optimization algorithm to imitate the behavior patterns of biological groups in nature, including but not limited to one of the ant colony algorithm and particle swarm optimization algorithm. The equipment and personnel at the construction site are regarded as a biological group, each individual has different attributes and behavioral capabilities, and the task allocation and action path of each individual are determined based on the quality assessment results and construction goals.

7. A construction engineering quality intelligent evaluation management method as claimed in claim 1, characterized in that: A temporary data processing center based on edge computing is set up at the construction site. When the network at the construction site fails or the delay is high, the data collected by the markers is first transmitted to the temporary data processing center for preliminary processing and analysis. The temporary data processing center uses a simplified causal inference model and quality evaluation algorithm to quickly process the data and generate a brief quality assessment report, which is promptly fed back to the management personnel at the construction site.

8. The intelligent evaluation and management method for construction engineering quality according to claim 1, characterized in that: Establish a construction experience knowledge base based on the knowledge graph, which will include but not be limited to the construction technology, quality problems, solutions and the relationships between various processes in historical building projects, and organize and store them in the form of a graph. When conducting quality evaluation and problem solving, relevant knowledge and experience can be quickly acquired through the query and reasoning functions of the knowledge graph.

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