Construction engineering prefabricated part analysis system based on digitization

Abstract

The invention relates to the technical field of engineering management, in particular to a digitization-based construction engineering prefabricated part analysis system which comprises the following steps: an informatization management module, a defect detection module, a performance analysis module, a durability evaluation module, an installation path planning module and an assembly error correction module. According to the method, digital management is introduced, the inventory of the prefabricated parts is effectively tracked and managed, detection of internal defects of the parts is combined, structural failure is recognized in the early stage, the construction safety is improved, decision support is provided for an engineering team by analyzing the performance of the prefabricated parts under the actual stress condition, the stability and reliability of the structure are ensured, and the construction efficiency is improved. By integrating environmental conditions and material characteristics, a data basis is provided for use and maintenance of components, time consumption and potential errors in the construction process are reduced through optimization of an assembly path, and by combining real-time error correction, the assembly precision is ensured, and the efficiency, economical efficiency and sustainability of an engineering project are enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of engineering management, and in particular to a digital-based construction engineering prefabricated component analysis system. Background Art

[0002] The field of engineering management technology involves multiple stages of engineering projects from conceptual design to completion and delivery. It combines engineering technology, project management and business management skills to ensure that engineering projects are completed efficiently, economically, on time and in compliance with regulations and quality standards. It includes project planning, cost management, time management, quality management, contract management and safety management. By applying modern management theories and a variety of software tools, it optimizes resource allocation and reduces waste, improves project efficiency and effectiveness, and helps project teams make effective decisions when faced with complex technical and organizational challenges.

[0003] Among them, the construction project prefabricated component analysis system is used to analyze the prefabricated components used in construction projects. It aims to improve the application efficiency and safety of prefabricated components in the construction process. By analyzing the structural performance, dimensional compliance, and installation feasibility of prefabricated components, it predicts structural problems, optimizes the use of components, and improves the overall quality and sustainability of engineering projects. It includes data collection, analysis modules, and visualization tools to help the team conduct detailed performance evaluations before component installation, enabling engineers and project managers to make decisions.

[0004] Traditional construction project prefabricated component analysis systems rely on traditional inventory management methods and lack real-time data updates and predictive analysis, resulting in excess or shortages of inventory, increasing project costs and affecting time management efficiency. Lack of defect detection and performance evaluation leads to failure to promptly discover material defects and performance problems during construction, increasing safety risks and the probability of project failure. The system fails to fully consider the impact of actual environmental conditions on the service life of materials, resulting in inaccurate durability assessments. The assembly process cannot be monitored and error corrected in real time, resulting in assembly errors being discovered only in the later stages of the project, increasing correction costs, and resulting in low efficiency in cost control, time management, and quality assurance of engineering projects, increasing the overall risk and uncertainty of the project. Summary of the invention

[0005] In order to solve the technical problem of digital level in the prior art, the embodiment of the present invention provides a construction engineering prefabricated component analysis system based on digitalization. The technical solution is as follows:

[0006] On the one hand, a digital construction engineering prefabricated component analysis system is provided, the system comprising:

[0007] The information management module matches identification numbers for multiple prefabricated components based on the inventory information of prefabricated components, combines the outbound data of various prefabricated components, identifies the trend of inventory changes, and generates digital management parameters;

[0008] The defect detection module detects the prefabricated components based on the digital management parameters using ultrasonic sensors to identify cracks, pores, and inclusions, and generates prefabricated component detection results;

[0009] The performance analysis module uses the prefabricated component detection results to monitor and analyze the deformation and stress distribution of the prefabricated components under various stress conditions, evaluate the performance levels of various prefabricated components, and generate performance evaluation information;

[0010] The durability assessment module analyzes the environmental conditions of the target prefabricated component installation site based on the performance evaluation information, calculates the aging rate and service life of the prefabricated component, and generates an environmental impact assessment result;

[0011] The installation path planning module uses the environmental impact assessment results to calculate and plan the assembly sequence and installation path according to the geometric shape, weight, and connection complexity of the components, and generates an assembly sequence planning result;

[0012] The assembly error correction module uses image acquisition equipment to monitor the installation position and docking status of components in real time based on the assembly sequence planning results, identifies geometric errors and performs calibration through image analysis, and generates assembly process monitoring information.

[0013] As a further solution of the present invention, the digital management parameters include inventory level change rate, prefabricated component number information, and inventory turnover rate; the prefabricated component inspection results include defect severity score, defect type information, and defect location coordinates; the performance evaluation information includes stress point distribution information, strain distribution diagram, and stress-strain curve; the environmental impact assessment results include expected degradation time, material environmental resistance index, and environmental risk level; the assembly sequence planning results include assembly step sequence, critical connection points, and expected assembly time; and the assembly process monitoring information includes real-time position error, installation process image set, and prefabricated component installation process record.

[0014] As a further solution of the present invention, the information management module matches identification numbers for multiple prefabricated components based on the prefabricated component inventory information, combines the outbound data of multiple prefabricated components, identifies the inventory change trend, and generates digital management parameters including:

[0015] The number automatic matching submodule is based on the inventory information of prefabricated components, and according to the type, specification, production batch and storage date information of the prefabricated components, assigns an identification number to each prefabricated component and generates a component identification number list;

[0016] The in-and-out data analysis submodule analyzes the inventory change trends of various prefabricated components based on the component identification number list and the component out-of-stock data, including the inventory consumption speed and periodic changes, and obtains the inventory change trend analysis results;

[0017] The inventory level monitoring submodule identifies the types of prefabricated components in excess and shortage based on the inventory change trend analysis results and in combination with target project requirements, and generates digital management parameters.

[0018] As a further solution of the present invention, the defect detection module detects the prefabricated components based on the digital management parameters using ultrasonic sensors to identify cracks, pores, and inclusions, and generates prefabricated component detection results including:

[0019] The scanning data acquisition submodule adjusts the ultrasonic sensor settings based on the digital management parameters, scans the prefabricated components, records the reflected waveforms, and obtains ultrasonic data;

[0020] The signal waveform analysis submodule pre-processes the ultrasonic signal based on the ultrasonic data, including filtering and waveform enhancement, to obtain processed waveform data;

[0021] The defect recognition submodule recognizes various defects in the internal structure of the target prefabricated component, including cracks, pores and inclusions, based on the processed waveform data, and generates a prefabricated component detection result.

[0022] As a further solution of the present invention, the specific formula for identifying multiple defects in the internal structure of the target prefabricated component is:

[0023]

[0024] Among them, f(x) is the classification function, which is used to predict the classification result based on the input data point x, sgn is the symbol function, which is used to convert the calculation result into a classification label, ∑ is the summation symbol, i is the index symbol, which is used to traverse all support vectors in the summation process, n is the total number of support vectors, and α i is the weight coefficient of the i-th support vector, y i is the class label of the i-th training sample, x i is the i-th support vector, which is the data point used to build the model during the training process. x is the input sample, which represents the data point that needs to be classified. b is the bias term, which is used to adjust the position of the classification hyperplane.

[0025] As a further solution of the present invention, the performance analysis module uses the prefabricated component detection results to monitor and analyze the deformation and stress distribution of the prefabricated components under various stress conditions, evaluate the performance levels of various prefabricated components, and generate performance evaluation information including:

[0026] The stress data acquisition submodule monitors and records the deformation of the prefabricated components under various stress conditions, including tension and compression, based on the prefabricated component detection results, and obtains stress and strain data;

[0027] The response state evaluation submodule calculates stress distribution based on the stress-strain data, evaluates the response state of the component under various loads, and generates stress distribution analysis results;

[0028] The multi-operating condition prediction submodule evaluates the performance level of the component and generates performance evaluation information by analyzing the performance of the target prefabricated component under various operating conditions based on the stress distribution analysis results.

[0029] As a further solution of the present invention, the durability assessment module analyzes the environmental conditions of the target prefabricated component installation site based on the performance evaluation information, calculates the aging rate and service life of the prefabricated component, and generates an environmental impact assessment result including:

[0030] The environmental condition analysis submodule collects a variety of environmental condition parameters, including temperature, humidity, and salt spray concentration, based on the performance evaluation information and the construction location information of the target project, and obtains environmental parameter analysis results;

[0031] The parameter analysis submodule analyzes the material properties of various prefabricated components based on the environmental parameter analysis results, including corrosion resistance, mechanical strength, and fire resistance, and obtains material property analysis results;

[0032] The service life prediction submodule calculates the aging rate of the prefabricated components under target environmental conditions based on the material property analysis results, predicts the expected service life, and generates environmental impact assessment results.

[0033] As a further solution of the present invention, the specific formula for predicting the expected service life is:

[0034]

[0035] Among them, τ represents the expected service life of the prefabricated component, T represents the average temperature in the target installation environment, H represents the average humidity in the environment, S represents the salt spray concentration, which evaluates the effect of salt in the environment on the corrosion rate of the material, k is the material's anti-aging constant, which characterizes the material's ability to resist aging, α is the adjustment coefficient for temperature influence, which is used to calibrate the effect of temperature on the aging rate, β is the adjustment coefficient for humidity influence, which is used to calibrate the effect of humidity on the aging rate, and γ is the adjustment coefficient for salt spray concentration, which is used to calibrate the effect of salt spray on the aging rate.

[0036] As a further solution of the present invention, the installation path planning module uses the environmental impact assessment results to calculate and plan the assembly sequence and installation path according to the geometric shape, weight, and connection complexity of the components, and generates the assembly sequence planning results including:

[0037] The assembly parameter analysis submodule analyzes the geometric shapes and weights of various prefabricated components based on the environmental impact assessment results, calculates the space, manpower and equipment requirements for the installation of multiple components, and obtains geometric parameter acquisition results;

[0038] The connection logic analysis submodule obtains the results based on the geometric parameters, identifies the connection points between the plurality of prefabricated components according to the architectural drawings, identifies the required installation sequence, and generates a connection sequence list;

[0039] The construction sequence analysis submodule plans the installation sequence of multiple prefabricated components based on the connection sequence list, takes into account the construction efficiency, combines the connection points between components and the assembly process requirements, and generates an assembly sequence planning result.

[0040] As a further solution of the present invention, the assembly error correction module uses an image acquisition device to monitor the installation position and docking status of the components in real time based on the assembly sequence planning result, identifies geometric errors and performs calibration through image analysis, and generates assembly process monitoring information including:

[0041] The real-time image acquisition submodule uses an image acquisition device to monitor the installation process of the components in real time and acquire assembly image data based on the assembly sequence planning result;

[0042] The error detection submodule analyzes the image based on the assembly image data, identifies the installation errors, including position offset and angle error, by comparing with the preset installation requirements, and generates error analysis results;

[0043] The correction prompt submodule matches the prompt message based on the error analysis result and the error data and sends it to the installer to generate assembly process monitoring information.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] By introducing digital management, we can effectively track and manage the inventory of prefabricated components. Combined with the detection of internal defects in components, structural failures can be identified at an early stage, improving construction safety. By analyzing the performance of prefabricated components under actual stress conditions, we provide decision-making support for the engineering team to ensure the stability and reliability of the structure. By integrating environmental conditions and material properties, we provide a data basis for the use and maintenance of components. The optimization of assembly paths reduces time consumption and potential errors in the construction process. Combined with real-time error correction, it ensures assembly accuracy and enhances the efficiency, economy and sustainability of engineering projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 is a system flow chart of the present invention;

[0048] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0049] Figure 3 It is a flow chart of the information management module of the present invention;

[0050] Figure 4 It is a flow chart of the defect detection module of the present invention;

[0051] Figure 5 It is a flow chart of the performance analysis module of the present invention;

[0052] Figure 6 is a flow chart of the durability assessment module of the present invention;

[0053] Figure 7 This is a flow chart of the installation path planning module of the present invention;

[0054] Figure 8 It is a flow chart of the assembly error correction module of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The present invention provides a digital construction engineering prefabricated component analysis system. Figure 1 to Figure 2 The present invention provides a technical solution, a digital construction engineering prefabricated component analysis system comprising:

[0061] The information management module matches identification numbers for multiple prefabricated components based on the inventory information of prefabricated components, combines the outbound data of various prefabricated components, identifies the trend of inventory changes, and generates digital management parameters;

[0062] The defect detection module uses ultrasonic sensors to detect prefabricated components based on digital management parameters, identify cracks, pores, and inclusions, and generate prefabricated component detection results;

[0063] The performance analysis module uses the prefabricated component test results to monitor and analyze the deformation and stress distribution of prefabricated components under various stress conditions, evaluate the performance levels of various prefabricated components, and generate performance evaluation information;

[0064] The durability assessment module analyzes the environmental conditions of the target prefabricated component installation site based on the performance evaluation information, calculates the aging rate and service life of the prefabricated components, and generates environmental impact assessment results;

[0065] The installation path planning module uses the environmental impact assessment results to calculate and plan the assembly sequence and installation path according to the geometric shape, weight, and connection complexity of the components, and generates the assembly sequence planning results;

[0066] The assembly error correction module uses image acquisition equipment to monitor the installation position and docking status of components in real time based on the assembly sequence planning results. Through image analysis, it identifies geometric errors and performs calibration to generate assembly process monitoring information.

[0067] Digital management parameters include inventory level change rate, prefabricated component number information, and inventory turnover rate. Prefabricated component inspection results include defect severity score, defect type information, and defect location coordinates. Performance evaluation information includes stress point distribution information, strain distribution diagram, and stress-strain curve. Environmental impact assessment results include expected degradation time, material environmental resistance index, and environmental risk level. Assembly sequence planning results include assembly step sequence, critical connection points, and expected assembly time. Assembly process monitoring information includes real-time position error, installation process image set, and prefabricated component installation process record.

[0068] See also Figure 2 and Figure 3 The information management module matches identification numbers for multiple prefabricated components based on the inventory information of prefabricated components, combines the outbound data of various prefabricated components, identifies the change trend of inventory, and generates digital management parameters including:

[0069] The number automatic matching submodule is based on the inventory information of prefabricated components, and according to the type, specification, production batch and storage date information of the prefabricated components, assigns an identification number to each prefabricated component and generates a component identification number list;

[0070] The database query and update operations are used to enter data for the prefabricated components entering the warehouse, including converting the component type, specification, production batch and entry date information into a database-compatible format, and using a hash function to generate an identifier. The process is completed through string operation functions and date and time functions in the programming language to ensure that each component can be accurately identified and tracked. Each record in the database will be assigned a logically constructed consecutive number, which is used for quick retrieval and reference within the system. The generated component identification number list is stored in a table form, containing multiple information and numbers of the component.

[0071] The in-and-out data analysis submodule analyzes the inventory change trends of various prefabricated components based on the component identification number list and the component out-of-stock data, including the inventory consumption speed and periodic changes, and obtains the inventory change trend analysis results;

[0072] Data analysis is performed through query language, including sorting and categorizing outbound data, and fitting and predicting time series data using statistical analysis software, including calculating inventory consumption rates and cyclical changes, involving moving average and exponential smoothing methods, determining the dynamic change patterns of inventory, and generating consumption reports and trend forecast graphs for each type and specification of prefabricated components. Target reports and charts help managers understand inventory liquidity and adjust inventory management strategies.

[0073] The inventory level monitoring submodule identifies the types of prefabricated components in excess and shortage based on the inventory change trend analysis results and the target project requirements, and generates digital management parameters;

[0074] Through data integration and analysis tools, inventory data and engineering requirements are compared to implement component type and quantity matching checks. The optimization module in the decision support system is used to adjust inventory configuration, including the use of linear programming and network flow analysis to optimize resource allocation, including adjusting inventory levels to match forecasted demand, issuing purchase or transfer recommendations, automatically updating inventory status, marking which components are in excess or shortage, and generating adjustment reports. The reports describe the optimization measures for each component type, including recommended purchase quantities and warehousing changes, to ensure the rational use of resources and the satisfaction of engineering requirements.

[0075] See also Figure 2 and Figure 4 The defect detection module uses ultrasonic sensors to detect prefabricated components based on digital management parameters, identify cracks, pores, and inclusions, and generate prefabricated component detection results including:

[0076] The scanning data acquisition submodule adjusts the ultrasonic sensor settings based on the digital management parameters, scans the prefabricated components, records the reflected waveforms, and obtains ultrasonic data;

[0077] The specific parameters of the ultrasonic sensor are adjusted through the user interface. The target parameters include scanning frequency, power and sensor movement speed to adapt to prefabricated components of different types and complexities. During the process, the sensor covers the entire surface of the component according to the set path, and the data acquisition software is used to record the waveform data reflected from the surface of the component. The target data reflects the structural characteristics inside the material. The data is stored in the form of digital signals, timestamped and preliminarily filtered for noise. The ultrasonic data obtained from each scan records the specific conditions and time of the scan to ensure data traceability. The target original ultrasonic data is stored on a high-performance server to provide a basis for signal processing and defect analysis.

[0078] The signal waveform analysis submodule pre-processes the ultrasonic signal based on the ultrasonic data, including filtering and waveform enhancement, to obtain processed waveform data;

[0079] Bandpass filters are applied to remove signal interference of irrelevant frequencies and waveform enhancement algorithms are used to improve data availability, including fast Fourier transform analysis of signal frequency domain characteristics and wavelet transform-based methods to extract useful information from signals. The processed data is optimized through algorithms to enhance the defect-sensitive parts of the reflected signal, making tiny cracks or internal pore defects easier to be identified by subsequent modules. Each processing result includes a comparison of the waveforms before and after processing and the changes in key parameters. The target information is extremely critical to understanding the internal state of the material and provides effective data support for accurately evaluating the integrity of the component.

[0080] The defect recognition submodule identifies various defects in the internal structure of the target prefabricated component, including cracks, pores and inclusions, based on the processed waveform data, and generates the prefabricated component inspection results;

[0081] The specific formula for identifying multiple defects in the internal structure of the target precast component is:

[0082]

[0083] Among them, f(x) is the classification function, which is used to predict the classification result based on the input data point x, sgn is the symbol function, which is used to convert the calculation result into a classification label, ∑ is the summation symbol, i is the index symbol, which is used to traverse all support vectors in the summation process, n is the total number of support vectors, and α i is the weight coefficient of the i-th support vector, y i is the class label of the i-th training sample, x i is the i-th support vector, which is the data point used to build the model during the training process. x is the input sample, which represents the data point that needs to be classified. b is the bias term, which is used to adjust the position of the classification hyperplane.

[0084] formula:

[0085]

[0086] Detailed explanation of the formula and the process of formula calculation and derivation:

[0087] The formula identifies defects inside precast components by weighting and judging the classification of data points;

[0088] Parameter meaning and setting value:

[0089] α i : The weight of the i-th support vector, assumed to be [0.5, 0.3, 0.2], reflects the influence of each support vector in the classification decision;

[0090] y i : The class label of the i-th training sample, assumed to be [1,-1,1], indicating the classification of the three samples;

[0091] x i : The i-th support vector, assumed to be [[2,3],[1,2],[3,1]], indicates the position of each data point in the feature space;

[0092] x: the feature vector of the input sample to be classified, assumed to be [1,2], indicating its position in the feature space;

[0093] b: Bias term, assumed to be 1.2, used to adjust the position of the classification hyperplane to adapt to different data distributions;

[0094] Substitute the parameters into the formula for calculation:

[0095] Calculate x i x:

[0096] x1·x=2×1+3×2=8-x2·x=1×1+2×2=5-x3·x=3×1+1×

[0097] 2 = 5;

[0098] Calculate f(x):

[0099]

[0100] f(x)=sgn(3.5+1.2)=sgn(4.7)=1;

[0101] A classification result of 1 indicates that the input sample is identified as having structural defects based on its waveform data characteristics. The numerical value is used to distinguish the waveform characteristics of normal areas from defective areas, providing key information for prefabricated component inspection.

[0102] See also Figure 2 and Figure 5 The performance analysis module uses the prefabricated component test results to monitor and analyze the deformation and stress distribution of prefabricated components under various stress conditions, evaluate the performance levels of various prefabricated components, and generate performance evaluation information including:

[0103] The stress data acquisition submodule monitors and records the deformation of prefabricated components under various stress conditions, including tension and compression, based on the prefabricated component inspection results, and obtains stress and strain data;

[0104] Stress sensors and displacement sensors are used to monitor the deformation of prefabricated components in stress tests in real time. The target sensors are fixed at key positions of the components to record stress and strain data in standardized tensile and compression tests. The output of the sensors is directly transmitted to the main control computer system through a data acquisition card. LabVIEW software is used for synchronous data acquisition and preliminary analysis. It is equipped with data recording and graphic display functions to help monitor the test progress in real time and make adjustments. The stress and strain data are recorded in the database. The data includes timestamp, stress value, strain value and test conditions. The elastic modulus and yield strength of the material are automatically calculated according to the material mechanics theory to provide original measurement data for data analysis and material performance evaluation.

[0105] The response state assessment submodule calculates stress distribution based on stress-strain data, assesses the response state of components under various loads, and generates stress distribution analysis results;

[0106] In the above content, based on the stress-strain data, the stress distribution is calculated by the formula and Evaluate the response of components under various loads;

[0107] Where σ is the stress of the target component, ∈ is the strain expression, F represents the applied force, A represents the force-bearing area, σ represents the stress, ΔL represents the length change, L0 represents the original length, and ∈ represents the strain;

[0108] Detailed explanation of the formula and the process of formula calculation and derivation:

[0109] Assume that the target prefabricated component is subjected to a force of 5000N during the test and the cross-sectional area is 0.005m 2 , the length of the component changes from 2m to 2.001m, calculate the stress σ:

[0110]

[0111] Calculate strain∈:

[0112]

[0113] The results σ = 1MPa and ∈ = 0.0005 show the stress and strain performance of the component under this load. The formula is used to predict the performance of the component in actual working conditions to ensure the suitability of building design and use.

[0114] The multi-condition prediction submodule is based on the stress distribution analysis results, analyzes the performance of the target prefabricated components under various working conditions, evaluates the performance level of the components, and generates performance evaluation information;

[0115] Regression analysis methods in machine learning, such as support vector machines and random forest algorithms, are used to predict the responses of components under different working conditions, including training and testing the data set first to ensure the accuracy and generalization ability of the model. During the analysis process, the model outputs the behavior prediction of prefabricated components under extreme environments, including maximum bearing capacity, fatigue life and failure mode. The generated performance evaluation information provides quantitative indicators for the reliability and safety of the components under each working condition. The generated report describes the overall performance evaluation of the components and the recommended operating restrictions.

[0116] See also Figure 2 and Figure 6 The durability assessment module analyzes the environmental conditions of the target prefabricated component installation site based on the performance evaluation information, calculates the aging rate and service life of the prefabricated components, and generates environmental impact assessment results including:

[0117] The environmental condition analysis submodule collects various environmental condition parameters, including temperature, humidity, and salt spray concentration, based on the performance evaluation information and the construction location information of the target project, and obtains the environmental parameter analysis results;

[0118] Use geographic information systems and sensor networks to collect environmental condition data at construction sites, including using geographic information systems to accurately map the climate data of target sites in space and time. At the same time, deploy environmental sensors to collect real-time temperature, humidity and salt spray concentration parameters. The target sensors are connected to the central monitoring system and transmit data through wireless networks. Use environmental monitoring software EnviroMon to analyze and record the collected data in real time. The software can display real-time data streams, generate historical trend charts and trigger environmental anomaly alarms. Use data cleaning and data normalization techniques to process the collected environmental data to ensure data accuracy and availability. Generate environmental parameter analysis results by analyzing target data. The results describe the environmental characteristics and environmental risks of the construction site, providing a scientific basis for material property analysis and service life prediction.

[0119] The parameter analysis submodule analyzes the material properties of various prefabricated components based on the environmental parameter analysis results, including corrosion resistance, mechanical strength, and fire resistance, and obtains the material property analysis results;

[0120] Apply material science principles and chemical analysis techniques to study the material response of different prefabricated components. In the process, electrochemical impedance spectroscopy and scanning electron microscopy are used to quantitatively and qualitatively analyze the corrosion resistance and mechanical strength of various materials, and evaluate the corrosion rate of materials under target environmental conditions. SEM provides images of the material microstructure, identifies microscopic defects and corrosion products of the material, and combines thermogravimetric analysis to determine the fire resistance of the material. By analyzing the changes in material mass at high temperatures, thermal stability is predicted. Through targeted advanced analytical techniques, material property analysis results are generated.

[0121] The service life prediction submodule calculates the aging rate of prefabricated components under target environmental conditions based on the material property analysis results, predicts the expected service life, and generates environmental impact assessment results;

[0122] The specific formula for predicting the expected useful life is:

[0123]

[0124] Among them, τ represents the expected service life of the prefabricated component, T represents the average temperature in the target installation environment, H represents the average humidity in the environment, S represents the salt spray concentration, which evaluates the effect of salt in the environment on the corrosion rate of the material, k is the material's anti-aging constant, which characterizes the material's ability to resist aging, α is the adjustment coefficient for temperature influence, which is used to calibrate the effect of temperature on the aging rate, β is the adjustment coefficient for humidity influence, which is used to calibrate the effect of humidity on the aging rate, and γ is the adjustment coefficient for salt spray concentration, which is used to calibrate the effect of salt spray on the aging rate.

[0125] formula:

[0126]

[0127] Detailed explanation of the formula and the process of formula calculation and derivation:

[0128] The formula is used to calculate the expected service life of precast components under specific environmental conditions;

[0129] Parameter meaning and setting value:

[0130] T is the average ambient temperature, assumed to be 25°C;

[0131] H is the average humidity of the environment, assumed to be 60%;

[0132] S is the salt spray concentration in the environment, assumed to be 5 mg / L;

[0133] k is a material property constant, assumed to be 10 years, representing the basic expected life of the material under ideal conditions without environmental stress;

[0134] α, β, and γ are the weight coefficients of temperature, humidity, and salt spray concentration, respectively, assuming that α = 0.05, β = 0.03, and γ = 0.02;

[0135] Substitute the parameters into the formula for calculation:

[0136]

[0137] The result 5.63 shows that under the environmental conditions of average temperature 25℃, average humidity 60% and salt spray concentration 5mg / L, the expected service life of prefabricated components is about 5.63 years. The calculation results are used to help engineers and project managers understand the durability and replacement cycle of prefabricated components under given environmental conditions.

[0138] See also Figure 2 and Figure 7 The installation path planning module uses the environmental impact assessment results to calculate and plan the assembly sequence and installation path according to the geometry, weight, and connection complexity of the components. The generated assembly sequence planning results include:

[0139] The assembly parameter analysis submodule analyzes the geometric shapes and weights of various prefabricated components based on the environmental impact assessment results, calculates the space, manpower and equipment requirements for the installation of multiple components, and obtains the geometric parameter acquisition results;

[0140] By inputting the size and material data of each component, building a three-dimensional model using CAD software, and using physical modeling tools to calculate the required support structure and assembly space, the spatial compatibility and structural stability of the component combination are automatically evaluated during the process to ensure that the provided solution is suitable for the theoretical model and meets the spatial and safety requirements of actual applications. The calculated manpower and equipment requirements are based on the preset labor efficiency and equipment capacity parameters. By simulating different assembly scenarios, the minimum required working space and optimal equipment configuration are evaluated, and the specific installation requirements of each component are obtained. The generated geometric parameter acquisition results list the spatial layout of each component, the required equipment type and the number of operators, providing accurate basic data for subsequent connection logic analysis.

[0141] The connection logic analysis submodule obtains results based on geometric parameters, identifies the connection points between multiple prefabricated components according to the architectural drawings, identifies the required installation sequence, and generates a connection sequence list;

[0142] Connection point analysis is performed using structural analysis software such as Tekla Structures. The software automatically identifies potential connection points between prefabricated components based on architectural drawings and geometric models, analyzes the structural strength and stability of the target connection points, optimizes the connection sequence, ensures the accessibility and safety of each connection point during construction, simulates the assembly sequence, predicts the structural response under different assembly sequences, optimizes connection efficiency and reduces construction risks, generates a connection sequence list, lists the optimized assembly path and specific operation steps for each connection point, and provides clear guidance for the construction team in actual operations, ensuring the smoothness of construction and the integrity of the structure.

[0143] The construction sequence analysis submodule plans the installation sequence of multiple prefabricated components based on the connection sequence list, taking into account the construction efficiency, combining the connection points between components and the assembly process requirements, and generates the assembly sequence planning results;

[0144] Use project management software such as Microsoft Project to plan and optimize the construction sequence, input engineering parameters including human resources, time constraints and technical requirements, apply critical path method and Monte Carlo simulation to analyze key factors and potential risks in the construction process, ensure the optimization of the construction sequence, and improve engineering efficiency and quality control by dynamically adjusting construction steps and resource allocation. The simulation results help project managers analyze the impact of different construction sequences on project completion time and cost, and provide data support for decision-making. The generated assembly sequence planning results describe the specific tasks, required resources and estimated schedule for each construction stage, provide a construction plan with high feasibility and low risk, and optimize the execution strategy of the entire construction project.

[0145] See also Figure 2 and Figure 8The assembly error correction module uses image acquisition equipment to monitor the installation position and docking status of components in real time based on the assembly sequence planning results. Through image analysis, it identifies geometric errors and calibrates them to generate assembly process monitoring information including:

[0146] The real-time image acquisition submodule uses image acquisition equipment to monitor the component installation process in real time and obtain assembly image data based on the assembly sequence planning results;

[0147] A high-definition camera system is used to monitor the assembly process of prefabricated components in real time. The target camera is installed at a key position to capture images in a panoramic and local detail manner to ensure that visual data of assembly activities are obtained from multiple angles. The collected image data is preliminarily processed by real-time video analysis software. Through a network connection, the target real-time data is transmitted to the central monitoring system for further analysis and storage. The focus and angle of the camera are adjusted according to the assembly sequence planning results to optimize image capture efficiency and ensure that all important assembly steps are accurately recorded. The acquired assembly image data accurately records the visual information of each step of the assembly, providing a rich source of original data for subsequent error detection.

[0148] The error detection submodule analyzes the image based on the assembly image data, identifies the installation errors, including position offset and angle error, by comparing with the preset installation requirements, and generates error analysis results;

[0149] Use edge detection, feature extraction and pattern recognition in digital image processing, including deep learning frameworks such as TensorFlow for deep image analysis, convert image data into a format suitable for analysis, and use pre-trained convolutional neural network models to identify multiple features in the image, such as the alignment of assembly interfaces and the integrity of fixed points. By comparing and analyzing with preset installation requirements, position offset and angle error assembly errors are identified. The errors found will record information, including error type, location, size and cause. The generated error analysis results provide precise quantitative data for assembly quality control. Target data is crucial for evaluating the accuracy and reliability of the assembly process, and provides a basis for quality improvement and risk management.

[0150] The correction prompt submodule matches the prompt message based on the error analysis results and the error data and sends it to the installer to generate assembly process monitoring information;

[0151] A customized message generation algorithm is used to automatically match and generate targeted prompt messages. The algorithm analyzes the severity and urgency of different types of errors, such as position offset or angle error, and selects the most suitable prompt or warning from the message library. Through the user interface, the target message is instantly sent to the installer at the assembly site. The communication technology used includes wireless networks and mobile device applications to ensure that information can be conveyed quickly and accurately. The system provides an interactive feedback mechanism that allows installers to confirm and respond to the instructions they receive, achieving two-way communication and generating assembly process monitoring information to ensure the smooth progress of the project.

[0152] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0153] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0154] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0155] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0156] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0158] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0159] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0161] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0162] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A digital construction engineering prefabricated component analysis system, characterized in that: The system comprises: The information management module matches identification numbers for multiple prefabricated components based on the inventory information of prefabricated components, combines the outbound data of various prefabricated components, identifies the change trend of inventory, and generates digital management parameters; The defect detection module detects the prefabricated components based on the digital management parameters using ultrasonic sensors to identify cracks, pores, and inclusions, and generates prefabricated component detection results; The performance analysis module uses the prefabricated component detection results to monitor and analyze the deformation and stress distribution of the prefabricated components under various stress conditions, evaluate the performance levels of various prefabricated components, and generate performance evaluation information; The durability assessment module analyzes the environmental conditions of the target prefabricated component installation site based on the performance evaluation information, calculates the aging rate and service life of the prefabricated component, and generates an environmental impact assessment result; The installation path planning module uses the environmental impact assessment results to calculate and plan the assembly sequence and installation path according to the geometric shape, weight, and connection complexity of the components, and generates an assembly sequence planning result; The assembly error correction module uses image acquisition equipment to monitor the installation position and docking status of components in real time based on the assembly sequence planning results, identifies geometric errors and performs calibration through image analysis, and generates assembly process monitoring information.

2. The digital construction engineering prefabricated component analysis system according to claim 1 is characterized in that: The digital management parameters include inventory level change rate, prefabricated component number information, and inventory turnover rate; the prefabricated component inspection results include defect severity score, defect type information, and defect location coordinates; the performance evaluation information includes stress point distribution information, strain distribution diagram, and stress-strain curve; the environmental impact assessment results include expected degradation time, material environmental resistance index, and environmental risk level; the assembly sequence planning results include assembly step sequence, key connection points, and expected assembly time; the assembly process monitoring information includes real-time position error, installation process image set, and prefabricated component installation process record.

3. The digital construction engineering prefabricated component analysis system according to claim 1 is characterized in that: The information management module matches identification numbers for multiple prefabricated components based on the prefabricated component inventory information, combines the outbound data of multiple prefabricated components, identifies the inventory change trend, and generates digital management parameters including: The number automatic matching submodule is based on the prefabricated component inventory information, according to the type, specification, production batch and storage date information of the prefabricated component, and assigns an identification number to each prefabricated component to generate a component identification number list; The in-and-out data analysis submodule analyzes the inventory change trends of various prefabricated components based on the component identification number list and the component out-of-stock data, including the inventory consumption speed and periodic changes, and obtains the inventory change trend analysis results; The inventory level monitoring submodule identifies the types of prefabricated components in excess and shortage based on the inventory change trend analysis results and in combination with target project requirements, and generates digital management parameters.

4. The digital construction engineering prefabricated component analysis system according to claim 1 is characterized in that: The defect detection module detects the prefabricated components based on the digital management parameters using ultrasonic sensors, identifies cracks, pores, and inclusions, and generates prefabricated component detection results including: The scanning data acquisition submodule adjusts the ultrasonic sensor settings based on the digital management parameters, scans the prefabricated components, records the reflected waveforms, and obtains ultrasonic data; The signal waveform analysis submodule pre-processes the ultrasonic signal based on the ultrasonic data, including filtering and waveform enhancement, to obtain processed waveform data; The defect recognition submodule recognizes various defects in the internal structure of the target prefabricated component, including cracks, pores and inclusions, based on the processed waveform data, and generates a prefabricated component detection result.

5. The digital construction engineering prefabricated component analysis system according to claim 4 is characterized in that: The specific formula for identifying multiple defects in the internal structure of the target prefabricated component is: Among them, f(x) is the classification function, which is used to predict the classification result based on the input data point x, sgn is the symbol function, which is used to convert the calculation result into a classification label, ∑ is the summation symbol, i is the index symbol, which is used to traverse all support vectors in the summation process, n is the total number of support vectors, and α i is the weight coefficient of the i-th support vector, y i is the class label of the i-th training sample, x i is the i-th support vector, which is the data point used to build the model during the training process. x is the input sample, which represents the data point that needs to be classified. b is the bias term, which is used to adjust the position of the classification hyperplane.

6. The digital construction engineering prefabricated component analysis system according to claim 1 is characterized in that: The performance analysis module uses the prefabricated component detection results to monitor and analyze the deformation and stress distribution of the prefabricated components under various stress conditions, evaluate the performance levels of various prefabricated components, and generate performance evaluation information including: The stress data acquisition submodule monitors and records the deformation of the prefabricated components under various stress conditions, including tension and compression, based on the prefabricated component detection results, and obtains stress and strain data; The response state evaluation submodule calculates stress distribution based on the stress-strain data, evaluates the response state of the component under various loads, and generates stress distribution analysis results; The multi-operating condition prediction submodule evaluates the performance level of the component and generates performance evaluation information by analyzing the performance of the target prefabricated component under various operating conditions based on the stress distribution analysis results.

7. The digital construction engineering prefabricated component analysis system according to claim 1 is characterized in that: The durability assessment module analyzes the environmental conditions of the target prefabricated component installation site based on the performance evaluation information, calculates the aging rate and service life of the prefabricated component, and generates an environmental impact assessment result including: The environmental condition analysis submodule collects a variety of environmental condition parameters, including temperature, humidity, and salt spray concentration, based on the performance evaluation information and the construction location information of the target project, and obtains environmental parameter analysis results; The parameter analysis submodule analyzes the material properties of various prefabricated components based on the environmental parameter analysis results, including corrosion resistance, mechanical strength, and fire resistance, and obtains material property analysis results; The service life prediction submodule calculates the aging rate of the prefabricated components under target environmental conditions based on the material property analysis results, predicts the expected service life, and generates environmental impact assessment results.

8. The digital construction engineering prefabricated component analysis system according to claim 7 is characterized in that: The specific formula for predicting the expected useful life is: Among them, τ represents the expected service life of the prefabricated component, T represents the average temperature in the target installation environment, H represents the average humidity in the environment, S represents the salt spray concentration, which evaluates the effect of salt in the environment on the corrosion rate of the material, k is the material's anti-aging constant, which characterizes the material's ability to resist aging, α is the adjustment coefficient for temperature influence, which is used to calibrate the effect of temperature on the aging rate, β is the adjustment coefficient for humidity influence, which is used to calibrate the effect of humidity on the aging rate, and γ is the adjustment coefficient for salt spray concentration, which is used to calibrate the effect of salt spray on the aging rate.

9. The digital construction engineering prefabricated component analysis system according to claim 1, characterized in that: The installation path planning module uses the environmental impact assessment results to calculate and plan the assembly sequence and installation path according to the geometric shape, weight, and connection complexity of the components, and generates the assembly sequence planning results including: The assembly parameter analysis submodule analyzes the geometric shapes and weights of various prefabricated components based on the environmental impact assessment results, calculates the space, manpower and equipment requirements for the installation of multiple components, and obtains geometric parameter acquisition results; The connection logic analysis submodule obtains the results based on the geometric parameters, identifies the connection points between the plurality of prefabricated components according to the architectural drawings, identifies the required installation sequence, and generates a connection sequence list; The construction sequence analysis submodule plans the installation sequence of multiple prefabricated components based on the connection sequence list, takes into account the construction efficiency, combines the connection points between components and the assembly process requirements, and generates an assembly sequence planning result.

10. The digital construction engineering prefabricated component analysis system according to claim 1, characterized in that: The assembly error correction module uses an image acquisition device to monitor the installation position and docking status of components in real time based on the assembly sequence planning result, identifies geometric errors and performs calibration through image analysis, and generates assembly process monitoring information including: The real-time image acquisition submodule uses an image acquisition device to monitor the installation process of the components in real time and acquire assembly image data based on the assembly sequence planning result; The error detection submodule analyzes the image based on the assembly image data, identifies the installation errors, including position offset and angle error, by comparing with the preset installation requirements, and generates error analysis results; The correction prompt submodule matches the prompt message based on the error analysis result and the error data and sends it to the installer to generate assembly process monitoring information.

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