Building construction quality safety intelligent management system

Abstract

The invention discloses a building construction quality safety intelligent management system, and relates to the technical field of reinforcing steel bar construction monitoring, and the system can intelligently identify and evaluate the binding quality of reinforcing steel bar groups by monitoring, regulating and controlling different stress characteristics of longitudinal and transverse reinforcing steel bar groups and stirrup groups in the reinforcing steel bar binding process. Different types of reinforcing steel bar groups are different in stress in the binding process, a reinforcing steel bar group prediction model is constructed, binding quality coefficients of various types of reinforcing steel bar groups are accurately evaluated, unqualified reinforcing steel bar groups are found in real time, corresponding strategies are generated in time, improvement measures are implemented, and stress distribution in the concrete pouring process is continuously and dynamically monitored. And analyzing the stress distribution coefficient of each reinforcing steel bar group, and evaluating the stress condition of each reinforcing steel bar group to generate a corresponding adjustment strategy. Due to the dynamic regulation and control, the phenomenon of local stress concentration or imbalance is reduced, and the combination effect of the reinforcing steel bars and the concrete is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of steel bar construction monitoring, and specifically to an intelligent management system for building construction quality and safety. Background Art

[0002] With the continuous expansion of the scale of modern construction projects, the quality management requirements of building construction are increasing day by day. As a key link in building structures, steel bar construction directly affects the safety and stability of buildings. However, during the steel bar construction process, due to the limitations of traditional manual inspection methods and technical means, it is difficult to comprehensively, real-time and accurately control the quality of steel bar binding. The traditional quality inspection methods for steel bar construction mainly rely on the experience judgment and manual inspection of on-site construction personnel. These methods not only have high labor intensity and low work efficiency, but are also easily interfered by human factors, resulting in inaccurate inspection results and the inability to timely detect potential quality problems, thus seriously affecting the quality of building projects.

[0003] In traditional construction management, the quality of the steel bar binding process is usually only inspected in the late stage or after completion of construction. If quality problems occur at this time, it may affect the subsequent concrete pouring process and even threaten the overall safety of the structure. At the same time, traditional technologies lack real-time monitoring of the stress distribution between steel bars and concrete during the concrete pouring process, and any problems during construction are often discovered only in the later stage, affecting the safety and construction progress of the project. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent management system for building construction quality and safety to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent management system for building construction quality and safety, including:

[0006] A steel bar construction binding monitoring module, which is used to set detection points at the construction site, collect the steel bar group binding data of several steel bar groups during the steel bar binding process, and according to the steel bar group binding data;

[0007] Using a convolutional neural network, a steel bar group prediction model is constructed, and after training the steel bar group binding data on the steel bar group prediction model, the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup steel bar group k are analyzed and obtained, and after evaluation, the corresponding evaluation results are obtained;

[0008] The first control module is used to identify the corresponding evaluation results. If the identified label is qualified, it outputs and summarizes the qualified steel bar groups and waits for concrete pouring;

[0009] If the identified corresponding evaluation result is an unqualified alarm, it generates and implements the corresponding strategy;

[0010] The pouring dynamic detection module is used to continuously monitor the concrete pouring for the qualified steel bar groups to establish a stress distribution data set, and based on the stress distribution data set, analyze and obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k ;

[0011] The second control module is used to evaluate the stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k to obtain the corresponding evaluation results and generate the corresponding strategies.

[0012] Preferably, the steel bar construction binding monitoring module includes a first acquisition unit and a first analysis unit;

[0013] The first acquisition unit is used to set detection points at the construction site. First, it acquires the steel bar group binding data of several steel bar groups during the steel bar binding process, and extracts the on-site binding parameters of the i-th longitudinal steel bar group, the j-th transverse steel bar group, and the k-th stirrup group according to the number of each steel bar group;

[0014] According to the on-site binding parameters, it extracts the number of turns of each binding point, establishes a binding turn number set, and marks it as: {T loop,1 , T loop,2 , T loop,3 ,..., T loop,y}, where T loop,1 to T loop,y represent the number of turns of the first binding point to the y-th binding point;

[0015] Through the binding equipment equipped with force sensors, it automatically records the tension data of the binding points, establishes a tension data set, and marks the tension values of multiple binding points as: {T tie,1 , T tie,2 , T tie,3 ,..., T tie,y}, where T tie,1 to T tie,y represent the tension values of the first binding point to the y-th binding point.

[0016] Preferably, the first analysis unit is used to extract the binding point data of each steel bar group in the set of binding turns, and calculate the average steel bar spacing S through the following formula JJ , the average number of turns of the binding belt T loop and the average tension of the binding belt T tie :

[0017]

[0018] In the formula, L is the length of the expected concrete member, Jr represents the end spacing, and N total represents the total number of steel bars in the steel bar group, d represents the diameter of the steel bar; L - 2Jr means subtracting the spacing at both ends from the total length of the member, and -N total ×d means subtracting the occupied space of each steel bar, specifically the product of the diameter of the steel bar and the total number; finally divided by N total -1, because it is to calculate the spacing between two steel bars, so 1 should be subtracted to obtain the average spacing value;

[0019] wherein, N represents the total number of binding points of the steel bar group, and T loop,y represents the number of turns at the yth binding point; T tie,y represents the tension value at the yth binding point;

[0020] The binding data of the steel bar group includes: the diameter of the steel bar d f , the total number of steel bars N total , the average spacing of the steel bars S JJ , the average number of turns of the binding belt T loop and the average tension of the binding belt T tie .

[0021] Preferably, the steel bar construction binding monitoring module further includes a model establishment unit;

[0022] The model establishment unit is used to use a convolutional neural network to construct an initial convolutional neural network model, train and test the initial convolutional neural network model with the binding data of the steel bar group, and use the middle layer output of the device operation state model as a feature vector to identify the feature information, and train and test the steel bar group prediction model with the obtained feature information, and use the trained steel bar group prediction model for data operation prediction to analyze and obtain the binding quality coefficient zBz of the ith longitudinal steel bar group i , the binding quality coefficient hBz of the jth transverse steel bar group j and the binding quality coefficient gBz of the kth stirrup group k ;

[0023] The calculation formula for the binding quality coefficient zBz of the ith longitudinal steel bar group i is as follows:

[0024]

[0025] Among them, represents the cross-sectional area of a single reinforcing bar in the i-th longitudinal reinforcing bar group, which affects the load-bearing capacity of the reinforcing bar group, N total,i represents the total number of reinforcing bars in the i-th longitudinal reinforcing bar group, indicating the total load, and the product of the two represents the load-bearing capacity of the entire longitudinal reinforcing bar; S JJ,i represents the average spacing of the reinforcing bars in the i-th longitudinal reinforcing bar group, which affects the uniformity of the force. The larger the spacing, the more dispersed the force; D tie,i represents the average spacing of the binding bands in the i-th longitudinal reinforcing bar group. The smaller the spacing, the tighter the binding and the higher the quality; T loop,i represents the average number of turns of the binding bands in the i-th longitudinal reinforcing bar group. The more the number of bindings, the better the stability; T tie,i represents the average tension of the binding bands in the i-th longitudinal reinforcing bar group; represents the tension term, taking 1 / 100 as the weight;

[0026] The binding quality coefficient hBz of the j-th transverse reinforcing bar group j The calculation formula is:

[0027]

[0028] Among them, The calculation logic is the same as that of the longitudinal reinforcing bars; represents the cross-sectional area of a single reinforcing bar in the j-th transverse reinforcing bar group, N total,j represents the total number of reinforcing bars in the j-th transverse reinforcing bar group;

[0029] (S JJ,j +D tie,j )×T loop,j Different from the longitudinal ones, (S JJ.j +D tie,j ) is an addition, rather than a multiplication, because the transverse reinforcing bars are less stressed and their binding quality is more easily affected by the spacing; therefore, directly add the average spacing S JJ.j of the reinforcing bars in the j-th transverse reinforcing bar group and the average spacing D tie,j of the binding bands to represent the combined effect of the two; T loop,j represents the average number of turns of the binding bands in the j-th transverse reinforcing bar group; the higher the number of turns of the binding, the more the number of bindings and the better the stability; T tie,j represents the average tension of the binding bands in the j-th transverse reinforcing bar group; represents the tension term. Since the binding of the transverse reinforcing bars is less sensitive to tension, the tension weight is reduced to 1 / 120;

[0030] The binding quality coefficient gBz of the k-th stirrup groupk The calculation formula is:

[0031]

[0032] Wherein, The calculation logic is the same as that of longitudinal steel bars and transverse steel bars; represents the cross-sectional area of a single steel bar in the k-th stirrup group, and N total,k represents the total number of steel bars in the k-th stirrup group;

[0033] S JJ,k represents the average spacing of steel bars in the k-th stirrup group; D tie,k represents the average spacing of binding bands in the k-th stirrup group; T loop,k represents the average number of turns of binding bands in the k-th stirrup group; S JJ,k ×D tie,k ×(T loop,k +1) is different from the transverse and longitudinal ones. (T loop,k +1) instead of T loop is for addition because stirrups are usually used in frame structures to bear large shear forces. The denser the binding, the higher the structural stability; therefore, an additional 1 is added to emphasize the influence of the number of turns on the quality; T tie,k represents the average tension of the binding bands in the k-th stirrup group; represents the tension term. Since the binding quality of stirrups depends more on tension, the weight of the tension term is set to 1 / 90, which is more important than 1 / 120 for transverse steel bars.

[0034] Preferably, the first regulation module includes a first evaluation unit and a first control unit; the first evaluation unit is used to preset a first threshold X1, a second threshold X2, and a third threshold X3; and compare the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup group k with the first threshold X1, the second threshold X2, and the third threshold X3 respectively to determine whether the quality of the steel bar group meets the standard, including:

[0035] The first threshold X1 includes a first qualified threshold and a first maximum threshold

[0036] Compare the binding quality coefficient zBz of the i-th longitudinal steel bar group i with the first threshold X1 to obtain a first evaluation result, including:

[0037] If It indicates that the binding quality of the longitudinal steel bar group is unqualified and too loose, resulting in unqualified structural strength and triggering the first unqualified alarm;

[0038] If It indicates that the binding quality of the longitudinal steel bar group is qualified, the binding is uniform and the strength meets the standard, which can not only meet the structural stability requirements but also avoid the waste of excessive tightness, and generate a qualified label;

[0039] If It indicates that the binding quality of the longitudinal steel bar group is unqualified and too tight, posing a risk of cost waste and affecting the pouring fluidity of concrete, and triggering the second unqualified alarm;

[0040] The second threshold X2 includes the second qualified threshold and the second maximum threshold

[0041] Compare the binding quality coefficient hBz of the j-th transverse steel bar group j with the second threshold X2 to obtain the second evaluation result, including:

[0042] If It indicates that the binding quality of the transverse steel bar group is unqualified and too loose, resulting in unqualified structural strength and triggering the third unqualified alarm;

[0043] If It indicates that the binding quality of the transverse steel bar group is qualified, the binding is uniform and the strength meets the standard, which can not only meet the structural stability requirements but also avoid the waste of excessive tightness, and generate a qualified label;

[0044] If It indicates that the binding quality of the transverse steel bar group is unqualified and too tight, posing a risk of cost waste and affecting concrete construction, and triggering the fourth unqualified alarm;

[0045] The third threshold X3 includes the third qualified threshold and the third maximum threshold

[0046] Compare the binding quality coefficient gBz of the k-th stirrup group k with the third threshold X3 to obtain the third evaluation result, including:

[0047] If It indicates that the binding quality of the stirrup group is unqualified and too loose, resulting in unqualified structural strength and triggering the fifth unqualified alarm;

[0048] If It indicates that the binding quality of the stirrup group is qualified, the binding is uniform and the strength meets the standard, which can not only meet the structural stability requirements but also avoid the waste of excessive tightness, and generate a qualified label;

[0049] If It indicates that the binding quality of this stirrup group is unqualified, too tight, there is a risk of cost waste and it affects the concrete construction, triggering the sixth unqualified alarm.

[0050] Preferably, the first control unit is used to identify the first evaluation result, the second evaluation result and the third evaluation result. For the binding quality coefficient zBz of the i-th longitudinal steel bar group that generates a qualified label i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup group k , they are summarized into the steel bar qualified group and wait for concrete pouring;

[0051] According to the first unqualified alarm, generate the first strategy, including: reducing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group to increase the density; increasing the average number of turns of the binding bands by 1-2 turns to improve the overall tightness of the steel bars; reducing the average spacing of the binding bands by 3%-6% to increase the tightness of the binding bands; increasing the average tension of the binding bands by 5%-10% to enhance the fastening effect of the binding bands;

[0052] According to the second unqualified alarm, generate the second strategy, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group and increasing the average spacing of the binding bands by 3%-6% to reduce the impact of excessive density;

[0053] According to the third unqualified alarm, generate the third strategy, including: reducing the average spacing of 3%-6% of the steel bars in this transverse steel bar group, increasing the average number of turns of the binding bands by 1-2 turns, reducing the average spacing of the binding bands by 3%-6%, and increasing the average tension of the binding bands by 5%-10%;

[0054] According to the fourth unqualified alarm, generate the fourth strategy, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group and increasing the average spacing of the binding bands by 3%-6%;

[0055] According to the fifth unqualified alarm, generate the fifth strategy, including: reducing the average spacing of 3%-6% of the steel bars in this stirrup group, increasing the average number of turns of the binding bands by 1-2 turns, reducing the average spacing of the binding bands by 3%-6%, and increasing the average tension of the binding bands by 5%-10%;

[0056] According to the sixth unqualified alarm, generate the sixth strategy, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group and increasing the average spacing of the binding bands by 3%-6%.

[0057] Preferably, the pouring dynamic monitoring module includes a second acquisition unit and a second analysis unit;

[0058] The second acquisition unit is used to extract the steel bar combination grid group, and after successfully matching the i-th longitudinal steel bar group, the j-th transverse steel bar group, and the k-th stirrup group waiting for concrete pouring with the positioning positions of each pouring component in the BIM model, carry out steel bar embedding and prepare for pouring;

[0059] During the pouring process, the concrete pouring speed, vibration intensity of the q-th pouring component, and the stress at the contact points between the steel bars and the concrete are monitored in real time, and then a stress distribution data set is established;

[0060] The second analysis unit is used to analyze and calculate the stress distribution coefficient zσ of the i-th longitudinal steel bar group based on the stress distribution data set i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k .

[0061] Preferably, the second analysis unit includes a longitudinal stress analysis unit, a transverse stress analysis unit, and a stirrup stress analysis unit;

[0062] The longitudinal stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group based on the stress distribution data set i , and the acquisition method is as follows:

[0063] S11. Extract the stress F at the m-th contact point of the i-th longitudinal steel bar group contact_i,m and the contact area A contact_i,m , after dimensionless processing, the stress value σ at the m-th contact point of the i-th longitudinal steel bar group is calculated through the following formula contact_i,m :

[0064]

[0065] S12. Perform weighted average on the ratio of the stress σ at the m-th contact point of the i-th longitudinal steel bar group to the average stress σ of all contact points contact_i,m , and combine the influence values of the concrete pouring speed V contact_avg and the vibration intensity I concrete on the stress distribution. After dimensionless processing, the stress distribution coefficient zσ of the i-th longitudinal steel bar group is obtained vibration : i :

[0066]

[0067] In the formula, σ contact_avg is the average stress of all contact points, N m represents all contact points of the i-th longitudinal steel bar group; K1 and K2 are respectively the concrete pouring speed V concreteand the vibration intensity I vibration influence coefficient;

[0068] The transverse stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient hσ of the j-th transverse steel bar group according to the stress distribution data set j , and the acquisition method is as follows:

[0069] S21. Extract the stress F of the m-th contact point of the j-th transverse steel bar group contact_j,m and the contact area A contact_j,m . After dimensionless processing, the stress value σ of the m-th contact point of the j-th transverse steel bar group is calculated by the following formula contact_j,m :

[0070]

[0071] S22. Perform weighted average on the ratio of the stress value σ of the m-th contact point of the j-th transverse steel bar group contact_j,m and the average stress σ of all contact points contact_avg , combine the concrete pouring speed V concrete and the vibration intensity I vibration influence value on stress distribution, and after dimensionless processing, obtain the stress distribution coefficient hσ of the j-th transverse steel bar group j :

[0072]

[0073] In the formula, σ contact_avg is the average stress of all contact points, and M m represents all contact points of the j-th transverse steel bar group;

[0074] The stirrup stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient gσ of the k-th stirrup group according to the stress distribution data set k , and the acquisition method is as follows:

[0075] S31. Extract the stress F of the m-th contact point of the k-th stirrup group contact_k,m and the contact area A contact_k,m . After dimensionless processing, the stress value σ of the m-th contact point of the k-th stirrup group is calculated by the following formula contact_k,m :

[0076]

[0077] S32. Perform weighted average on the ratio of the stress value σ of the m-th contact point of the k-th stirrup group contact_k,m and the average stress σ of all contact points contact_avg , combine the concrete pouring speed V concrete and the vibration intensity Ivibration After dimensionless processing of the influence value on the stress distribution, the stress distribution coefficient gσ of the k-th stirrup group is obtained k :

[0078]

[0079] In the formula, σ contact_avg is the average stress of all contact points, and G m represents all contact points of the k-th stirrup group.

[0080] Preferably, the second regulation module includes a second evaluation unit and a second control unit;

[0081] The second evaluation unit is used to preset a stress uniformity threshold Y, and the stress uniformity threshold Y includes a first stress uniformity threshold Y1, a second stress uniformity threshold Y2, and a third stress uniformity threshold Y3. The stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k are respectively compared and analyzed with the corresponding first stress uniformity threshold Y1, second stress uniformity threshold Y2, and third stress uniformity threshold Y3 to obtain a fourth evaluation result, including:

[0082] If zσ i ≥Y1, it means that the stress distribution uniformity of the i-th longitudinal steel bar group is qualified, and a qualified label is generated;

[0083] If zσ i <Y1, it means that the stress distribution uniformity of the i-th longitudinal steel bar group is unqualified, and the seventh unqualified alarm is triggered;

[0084] If hσ j ≥Y2, it means that the stress distribution uniformity of the j-th transverse steel bar group is qualified, and a qualified label is generated;

[0085] If hσ j <Y2, it means that the stress distribution uniformity of the j-th transverse steel bar group is unqualified, and the eighth unqualified alarm is triggered;

[0086] If gσ k ≥Y3, it means that the stress distribution uniformity of the k-th stirrup group is qualified, and a qualified label is generated;

[0087] If gσ k <Y3, it means that the stress distribution uniformity of the k-th stirrup group is unqualified, and the ninth unqualified alarm is triggered.

[0088] Preferably, the second control unit is used to generate a corresponding control strategy according to the fourth evaluation result, including:

[0089] Identify the fourth evaluation result, which is the stress distribution coefficient zσ of the i-th longitudinal steel bar group for generating qualified labels i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k , and summarize them into the construction qualified group; and receive the seventh unqualified alarm, the eighth unqualified alarm, and the ninth unqualified alarm triggered in the fourth evaluation result, and generate corresponding strategies, including:

[0090] According to the seventh unqualified alarm, generate the seventh strategy, including: adjusting the current concrete pouring speed to slow down by 3%-4%, and regulating the vibration intensity to be 5000-5500 times per minute, and increasing the vibration time of each layer of concrete to 15-20 seconds; and adding a steel mesh with an increase of 2%-3%;

[0091] According to the eighth unqualified alarm, generate the seventh strategy, including: adjusting the current concrete pouring speed to slow down by 5%-6%, and regulating the vibration intensity to be 5600-6000 times per minute, and increasing the vibration time of each layer of concrete to 21-25 seconds; and adding a steel mesh with an increase of 4%-5%;

[0092] According to the ninth unqualified alarm, generate the ninth strategy, including: adjusting the current concrete pouring speed to slow down by 7%-8%, and regulating the vibration intensity to be 6100-6500 times per minute, and increasing the vibration time of each layer of concrete to 26-30 seconds; and adding a steel mesh with an increase of 6%-7%.

[0093] The present invention provides an intelligent management system for construction quality and safety. It has the following beneficial effects:

[0094] (1) During the steel bar binding process, by precisely monitoring and regulating the different stress characteristics of the longitudinal, transverse steel bar groups and stirrup groups, this system can intelligently identify and evaluate the binding quality of the steel bar groups. Different types of steel bar groups are stressed differently during the binding process. The system constructs a prediction model for the steel bar groups through a convolutional neural network (CNN) to accurately evaluate the binding quality coefficient of various steel bar groups. The intelligent evaluation and regulation mechanism can real-time detect unqualified steel bar groups, generate corresponding strategies in a timely manner, and implement improvement measures to ensure that the steel bar construction quality meets the design requirements, thereby improving the quality control level during the construction process and avoiding potential safety hazards caused by improper steel bar binding.

[0095] (2) After the steel bar combination grid is formed, the system continuously monitors the stress distribution during the concrete pouring process in real time, collects stress distribution data, and analyzes the stress distribution coefficients of each steel bar group. Through the second regulation module, the system can evaluate the stress conditions of each steel bar group according to the real-time stress data and generate corresponding adjustment strategies based on the evaluation results. Such dynamic regulation can ensure uniform stress distribution during the pouring process, avoid local stress concentration or imbalance, optimize the bonding effect between steel bars and concrete, and enhance the overall stability and safety of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 It is a schematic flow chart of the intelligent management system for construction quality and safety of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0097] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0098] Embodiment 1

[0099] Please refer to Figure 1 , the present invention provides an intelligent management system for construction quality and safety, including:

[0100] A steel bar construction binding monitoring module, which is used to set detection points at the construction site, collect the steel bar group binding data of several steel bar groups during the steel bar binding process, and according to the steel bar group binding data;

[0101] Using a convolutional neural network, a steel bar group prediction model is constructed, and after training the steel bar group binding data on the steel bar group prediction model, the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup group k are analyzed and obtained, and after evaluation, the corresponding evaluation results are obtained;

[0102] A first regulation module, which is used to identify the corresponding evaluation results. If the identified label is a qualified label, it outputs and summarizes it into a steel bar combination qualified group and waits for concrete pouring;

[0103] If the identified corresponding evaluation result is an unqualified alarm, a corresponding strategy is generated and implemented;

[0104] The pouring dynamic detection module is used to continuously monitor the concrete pouring for the steel bar combination grid group, so as to establish a stress distribution data set, and analyze and obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group according to the stress distribution data set. i The stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group. k ;

[0105] The second regulation module is used to evaluate the stress distribution coefficient zσ of the i-th longitudinal steel bar group i the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k to obtain the corresponding evaluation results and generate the corresponding strategies.

[0106] In this embodiment, the traditional method for monitoring the quality of steel bar binding relies on manual inspection and empirical judgment, which is prone to missed inspection and misjudgment, with low efficiency, high labor costs and quality control risks. The present invention sets up multiple detection points at the construction site, collects data during the steel bar binding process in real time, and uses a convolutional neural network (CNN) to construct a prediction model for steel bar groups, which can intelligently analyze the binding quality of each steel bar group. This intelligent monitoring technology eliminates the interference of human factors, can obtain the binding quality coefficients of the i-th longitudinal steel bar group, the j-th transverse steel bar group and the k-th stirrup group in real time during the construction process, and conducts accurate evaluation, thereby improving the detection accuracy and efficiency of the quality of steel bar construction.

[0107] In traditional construction management, quality problems of steel bar binding are often discovered only in the late stage of construction or after completion, which may lead to unforeseen quality problems or safety hazards in subsequent construction. The system of the present invention can identify the evaluation results of the quality of steel bar binding in real time through the first regulation module, and according to the evaluation results, if unqualified situations are found, it can automatically generate targeted strategies and make adjustments. For example, the system can issue an alarm in real time and take corresponding corrective measures, such as readjusting the steel bar binding method, adjusting the quantity of steel bars, strengthening the monitoring of certain positions, etc. This real-time early warning and automatic intervention method can avoid large-scale rework and construction safety hazards in the later stage due to quality problems, thus effectively reducing the quality risks during the construction process.

[0108] During the traditional construction process, the monitoring of the stress distribution after concrete pouring often has a lag, making it difficult for construction workers to detect problems such as uneven stress in a timely manner, which can easily affect the safety of the building structure. In the present invention, the stress distribution of the steel bar combination grid group during the concrete pouring process is continuously monitored through the pouring dynamic detection module. By establishing a stress distribution data set, the system can obtain the stress distribution coefficients of the i-th longitudinal steel bar group, the j-th transverse steel bar group, and the k-th stirrup group in real time, and perform analysis to timely identify possible uneven stress or abnormal conditions. Through this real-time monitoring method, the system can ensure uniform stress distribution between the steel bars and the concrete, effectively avoiding potential safety hazards such as cracks and structural deformations caused by uneven stress.

[0109] During the pouring process of steel bars and concrete, the uniformity of stress distribution is crucial for the stability of the building. In the present invention, the second regulation module further evaluates the stress distribution data collected in real time, and automatically generates an optimization strategy for the detected uneven stress. For example, the system can adjust the pouring speed of the concrete, the vibration intensity, and even add a steel mesh or steel plate according to the stress distribution coefficients of the steel bar groups, so as to balance the stress distribution and improve the overall strength and stability of the concrete. This dynamic adjustment mechanism based on real-time data improves the flexibility and accuracy of the construction process, can intervene in potential problems in a timely manner during construction, and avoids the costs and time of later repairs.

[0110] Embodiment 2

[0111] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the steel bar construction binding monitoring module includes a first acquisition unit and a first analysis unit;

[0112] The first acquisition unit is used to set detection points at the construction site, first collect the steel bar group binding data of several steel bar groups during the steel bar binding process, and extract the on-site binding parameters of the i-th longitudinal steel bar group, the j-th transverse steel bar group, and the k-th stirrup group according to the number of each steel bar group;

[0113] According to the on-site binding parameters, the number of turns of each binding point is extracted, and a binding turn number set is established, marked as: {T loop,1 、T loop,2 、T loop,3 、...、T loop,y}, where T loop,1 to T loop,y represent the number of turns of the first binding point to the y-th binding point;

[0114] Through the binding equipment equipped with force sensors, the tension data of the binding points are automatically recorded, a tension data set is established, and the tension values of multiple binding points are marked as: {T tie,1 、Ttie,2 , T tie,3 ,..., T tie,y},T tie,1 to T tie,y represents the tension values from the first lashing point to the y-th lashing point.

[0115] The first analysis unit is used to extract the lashing point data of each steel bar group in the set of lashing numbers, and calculate the average steel bar spacing S JJ , the average number of lashing band turns T loop and the average tension of the lashing band T tie :

[0116]

[0117] In the formula, L is the length of the expected concrete member, Jr represents the end spacing, N total represents the total number of steel bars in the steel bar group, d represents the diameter of the steel bar; L - 2Jr means subtracting the spacing at both ends from the total length of the member, -N total ×d means subtracting the occupied space of each steel bar, specifically the product of the diameter of the steel bar and the total number; finally divided by N total -1, because it is to calculate the spacing between two steel bars, so 1 should be subtracted to get the average spacing value;

[0118] Among them, N represents the total number of lashing points of the steel bar group, T loop,y represents the number of turns at the y-th lashing point; T tie,y represents the tension value at the y-th lashing point;

[0119] The lashing data of the steel bar group includes: the diameter of the steel bar d f , the total number of steel bars N total , the average spacing of the steel bars S JJ , the average number of turns of the lashing band T loop and the average tension of the lashing band T tie . The lashing data of the steel bar group is obtained by measuring with a laser rangefinder, computer vision and image recognition algorithms, strain gauges, force sensors and tension sensors.

[0120] In this embodiment, the traditional method for controlling the quality of steel bar lashing relies on manual inspection, which has the problem of incomplete or inaccurate data collection. The present invention can accurately collect key data during the lashing process of the steel bar group, including the number of lashing turns, tension data, steel bar spacing, etc. by setting detection points at the construction site and combining with lashing equipment equipped with force sensors. The automatically recorded tension data and the set of lashing numbers not only reduce the errors and omissions of manual inspection, but also can monitor the tension changes of each lashing point in real time throughout the process, ensuring that each steel bar group meets the design requirements during the lashing process and controlling the construction quality from the source.

[0121] The first analysis unit of the present invention uses the collected steel bar binding data, through an intelligent analysis method, to calculate and extract key parameters such as the average spacing of steel bars, the average number of turns of binding straps, and the average tension of binding straps. By using formulas for calculation, the system can automatically evaluate the binding quality of each steel bar group, avoiding the limitations of relying on manual inspection and empirical judgment in traditional methods. This intelligent analysis can achieve efficient and accurate quality assessment, provide data support for subsequent construction decisions, and improve the controllability of construction quality. During the traditional steel bar binding process, quality problems are often only discovered during later inspections, and usually have already affected the progress and quality of subsequent construction by then. However, through the system of the present invention, all binding parameters (such as steel bar spacing, number of binding turns, and tension data) can be collected and analyzed in real time. If it is found that some binding parameters do not meet the predetermined standards, the system can immediately identify and issue an alarm, reminding the construction personnel to make timely adjustments, avoiding the spread and impact of quality problems, and ensuring that the quality of steel bar binding meets the specification requirements.

[0122] Example 3

[0123] This embodiment is an explanatory description carried out in Example 2. Please refer to Figure 1 , specifically, the steel bar construction binding monitoring module further includes a model establishment unit;

[0124] The model establishment unit is used to utilize a convolutional neural network to construct an initial convolutional neural network model, train and test the initial convolutional neural network model with the steel bar group binding data, and use the trained initial convolutional neural network model as the steel bar group prediction model. At the same time, the intermediate layer output of the equipment operation state model is used as a feature vector to identify feature information, and the steel bar group prediction model is trained and tested with the obtained feature information. The trained steel bar group prediction model is used for data operation prediction to analyze and obtain the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup group; k ;

[0125] The calculation formula for the binding quality coefficient zBz of the i-th longitudinal steel bar group is: i :

[0126]

[0127] Wherein, represents the cross-sectional area of a single steel bar in the i-th longitudinal steel bar group, which affects the stress-bearing capacity of the steel bar group, N total,i represents the total number of steel bars in the i-th longitudinal steel bar group, representing the total stress. The multiplication of these two represents the load-bearing capacity of the entire longitudinal steel bar; S JJ,iDenote the average spacing of steel bars in the $i$-th longitudinal steel bar group, which affects the uniformity of stress. The larger the spacing, the more dispersed the stress; $D$ tie,i Denote the average spacing of binding bands in the $i$-th longitudinal steel bar group. The smaller the spacing, the tighter the binding and the higher the quality; $T$ loop,i Denote the average number of turns of binding bands in the $i$-th longitudinal steel bar group. The more times of binding, the better the stability; $T$ tie,i Denote the average tension of binding bands in the $i$-th longitudinal steel bar group; Denote the tension term, taking 1 / 100 as the weight;

[0128] The binding quality coefficient $h_{Bz}$ of the $j$-th transverse steel bar group j The calculation formula is:

[0129]

[0130] Among them, The calculation logic is the same as that of longitudinal steel bars; Denote the cross-sectional area of a single steel bar in the $j$-th transverse steel bar group, $N$ total,j Denote the total number of steel bars in the $j$-th transverse steel bar group;

[0131] $(S$ JJ,j +$D$ tie,j )×$T$ loop,j Different from the longitudinal direction, $(S$ JJ.j +$D$ tie,j ) is for addition instead of multiplication because the stress on transverse steel bars is smaller and their binding quality is more easily affected by the spacing. Therefore, directly add the average spacing $S$ JJ.j of steel bars in the $j$-th transverse steel bar group and the average spacing $D$ tie,j of binding bands to represent the combined effect of the two; $T$ loop,j Denote the average number of turns of binding bands in the $j$-th transverse steel bar group; The higher the number of turns of binding, the more times of binding and the better the stability; $T$ tie,j Denote the average tension of binding bands in the $j$-th transverse steel bar group; Denote the tension term. Since the binding of transverse steel bars is less sensitive to tension, the tension weight is reduced to 1 / 120;

[0132] The binding quality coefficient $g_{Bz}$ of the $k$-th stirrup group k The calculation formula is:

[0133]

[0134] Among them, The calculation logic is the same as that of longitudinal steel bars and transverse steel bars; Denote the cross-sectional area of a single steel bar in the $k$-th stirrup group, $N$ total,k Denote the total number of steel bars in the $k$-th stirrup group;

[0135] S JJ,k represents the average spacing of steel bars in the k-th stirrup group; D tie,k represents the average spacing of binding straps in the k-th stirrup group; T loop,k represents the average number of turns of binding straps in the k-th stirrup group; S JJ,k ×D tie,k ×(T loop,k +1) is different from the horizontal and vertical directions, (T loop,k +1) instead of T loop is for addition, because stirrups are usually used in frame structures to bear large shear forces. The denser the binding, the higher the structural stability; therefore, an extra 1 is added to emphasize the impact of the number of turns on the quality; T tie,k represents the average tension of the binding straps in the k-th stirrup group; represents the tension term. Since the binding quality of stirrups is more dependent on tension, the weight of the tension term is set to 1 / 90, which is more important than 1 / 120 for horizontal steel bars.

[0136] Why are the calculation formulas for the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th horizontal steel bar group j and the binding quality coefficient gBz of the k-th stirrup group k different? Because of the following situations:

[0137] The stress conditions are different: Longitudinal steel bars mainly bear tensile forces, so the binding quality is greatly affected by the binding density;

[0138] Horizontal steel bars are mainly used to disperse loads, so the influence of the binding strap spacing is more obvious than that of the longitudinal direction. Therefore, it is (S JJ.j +D tie,j );

[0139] Stirrups are used in frame structures and have a great impact on stability. Therefore, an extra 1 is added to emphasize the role of the number of turns; The binding of longitudinal and stirrup steel bars is more affected by the number of binding turns, so the formula uses a multiplicative relationship; Horizontal steel bars are more sensitive to the binding spacing, so the spacing uses addition;

[0140] The following is an example chart of the steel bar groups with sample numbers:

[0141]

[0142]

[0143] Example of the calculation formula for sample number 1:

[0144] Sample number 1; Steel bar diameter (mm): 10; Total number of steel bars (pieces): 40; Average spacing of steel bars (mm): 150; Average spacing of binding bands (mm): 100; Average number of binding loops: 2; Average tension of binding bands (N): 50;

[0145]

[0146] Substitute the values for calculation:

[0147]

[0148] Example of the calculation formula for sample number 5:

[0149] Sample number 5; Steel bar diameter (mm): 16; Total number of steel bars (pieces): 40; Average spacing of steel bars (mm): 150; Average spacing of binding bands (mm): 100; Average number of binding loops: 2; Average tension of binding bands (N): 65;

[0150]

[0151] Substitute the values for calculation:

[0152]

[0153] Sample number: 9; Steel bar diameter (mm): 8; Total number of steel bars (pieces): 47; Average spacing of steel bars (mm): 160; Average spacing of binding bands (mm): 110; Average number of binding loops: 2; Average tension of binding bands (N): 90;

[0154]

[0155] Substitute the values for calculation:

[0156]

[0157] In this embodiment, by establishing an initial Convolutional Neural Network (CNN) model and training and testing the model with the data of steel bar group binding, accurate prediction of the quality of steel bar binding can be achieved. The trained model can analyze and evaluate the quality coefficients of different steel bar groups based on various influencing factors (such as cross-sectional area of steel bars, total number of steel bars, steel bar spacing, number of binding belt loops, binding belt tension, etc.). Specifically, the calculation formulas for the quality coefficients of longitudinal steel bar groups, transverse steel bar groups, and stirrup groups are quantified based on various factors such as the mechanical properties of steel bars, binding technology, and force uniformity. The calculation formulas for the binding quality coefficients of longitudinal steel bars, transverse steel bars, and stirrups respectively consider multiple factors such as the force-bearing capacity of steel bars, force uniformity, spacing of binding belts, number of binding belt loops, and tension of binding belts. The consideration of these comprehensive factors makes the evaluation of steel bar quality more comprehensive and accurate, covering all key links in the process of steel bar binding. The evaluation of the binding quality of stirrups focuses more on structural stability and shear resistance, while the evaluation of longitudinal and transverse steel bars focuses on their force-bearing capacity and force distribution. This differentiated evaluation method can better conform to the actual functions of different steel bar groups and improve the pertinence of quality evaluation.

[0158] Example 4

[0159] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the first regulation module includes a first evaluation unit and a first control unit; the first evaluation unit is used to preset a first threshold X1, a second threshold X2, and a third threshold X3; and compare the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup group k with the first threshold X1, the second threshold X2, and the third threshold X3 respectively to determine whether the quality of the steel bar group meets the standard, including:

[0160] The first threshold X1 includes a first qualified threshold and a first maximum threshold

[0161] Compare the binding quality coefficient zBz of the i-th longitudinal steel bar group i with the first threshold X1 to obtain a first evaluation result, including:

[0162] If it means that the binding quality of this longitudinal steel bar group is unqualified and too loose, resulting in unqualified structural strength, triggering a first unqualified alarm;

[0163] If it means that the binding quality of this longitudinal steel bar group is qualified, the binding is uniform and the strength meets the standard, which can not only meet the requirements of structural stability but also avoid the waste of excessive tightness, generating a qualified label;

[0164] If it indicates that the binding quality of the longitudinal steel bar group is unqualified, too tight, there is a risk of wasting costs and it affects the pouring fluidity of concrete, triggering the second unqualified alarm;

[0165] The second threshold X2 includes the second qualified threshold and the second maximum threshold

[0166] Compare the binding quality coefficient hBz of the jth transverse steel bar group j with the second threshold X2 to obtain the second evaluation result, including:

[0167] If it indicates that the binding quality of the transverse steel bar group is unqualified, too loose, resulting in unqualified structural strength, triggering the third unqualified alarm;

[0168] If it indicates that the binding quality of the transverse steel bar group is qualified, evenly bound and the strength meets the standard, which can not only meet the structural stability requirements but also avoid the waste of being too tight, generating a qualified label;

[0169] If it indicates that the binding quality of the transverse steel bar group is unqualified, too tight, there is a risk of wasting costs and it affects the concrete construction, triggering the fourth unqualified alarm;

[0170] The third threshold X3 includes the third qualified threshold and the third maximum threshold

[0171] Compare the binding quality coefficient gBz of the kth stirrup group k with the third threshold X3 to obtain the third evaluation result, including:

[0172] If it indicates that the binding quality of the stirrup group is unqualified, too loose, resulting in unqualified structural strength, triggering the fifth unqualified alarm;

[0173] If it indicates that the binding quality of the stirrup group is qualified, evenly bound and the strength meets the standard, which can not only meet the structural stability requirements but also avoid the waste of being too tight, generating a qualified label;

[0174] If it indicates that the binding quality of the stirrup group is unqualified, too tight, there is a risk of wasting costs and it affects the concrete construction, triggering the sixth unqualified alarm.

[0175] In this embodiment, through reasonable quality standards and threshold settings, problems such as structural instability or material waste caused by too loose or too tight binding quality can be effectively avoided, thus ensuring the construction quality and enhancing the safety and stability of the structure. Based on the evaluation and comparison of the binding quality coefficients of the steel bar groups, automated quality monitoring can be achieved, reducing manual inspection errors and improving the accuracy and efficiency of inspection. Through reasonable binding quality control, cost waste caused by over-tight binding is avoided, while ensuring the stability of binding, avoiding negative impacts on concrete construction, and optimizing the resource utilization efficiency.

[0176] Embodiment 5

[0177] This embodiment is an explanatory description carried out in Embodiment 4. Please refer to Figure 1 , specifically, the first control unit is used to identify the first evaluation result, the second evaluation result, and the third evaluation result. For the binding quality coefficient zBz of the i-th longitudinal steel bar group that generates a qualified label i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup group k , they are summarized into the qualified group of steel bar combinations and wait for concrete pouring;

[0178] According to the first unqualified alarm, a first strategy is generated, including: reducing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group to increase the density; increasing the average number of turns of the binding bands by 1-2 turns to improve the overall tightness of the steel bars; reducing the average spacing of the binding bands by 3%-6% to increase the tightness of the binding bands; increasing the average tension of the binding bands by 5%-10% to enhance the fastening effect of the binding bands;

[0179] According to the second unqualified alarm, a second strategy is generated, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group and increasing the average spacing of the binding bands by 3%-6% to reduce the impact of excessive density;

[0180] According to the third unqualified alarm, a third strategy is generated, including: reducing the average spacing of 3%-6% of the steel bars in this transverse steel bar group, increasing the average number of turns of the binding bands by 1-2 turns, reducing the average spacing of the binding bands by 3%-6%, and increasing the average tension of the binding bands by 5%-10%;

[0181] According to the fourth unqualified alarm, a fourth strategy is generated, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group and increasing the average spacing of the binding bands by 3%-6%;

[0182] According to the fifth unqualified alarm, a fifth strategy is generated, including: reducing the average spacing of 3%-6% of the steel bars in this stirrup group, increasing the average number of turns of the binding bands by 1-2 turns, reducing the average spacing of the binding bands by 3%-6%, and increasing the average tension of the binding bands by 5%-10%;

[0183] According to the sixth unqualified alarm, generate the sixth strategy, including: increasing the average spacing of 3%-6% of the steel bars in the longitudinal steel bar group and increasing the average spacing of the binding straps by 3%-6%.

[0184] In this embodiment, through different strategies generated for different types of steel bar groups (longitudinal steel bars, transverse steel bars, and stirrups), targeted adjustments can be made to ensure that the quality of steel bar binding meets the standards. For example, by adjusting parameters such as steel bar spacing, the number of binding strap loops, and the tightness of the binding straps, the binding quality can be effectively improved, avoiding insufficient structural strength or waste of resources. According to different alarms, the system can automatically generate specific control strategies, timely adjust the construction plan, and reduce potential structural safety hazards caused by unqualified binding quality. For example, when it is found that the binding of the longitudinal steel bar group is too loose, a strategy is generated to increase the tightness of the binding straps and the steel bar density to avoid the risk of unqualified structural strength.

[0185] Embodiment 6

[0186] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the pouring dynamic monitoring module includes a second acquisition unit and a second analysis unit;

[0187] The second acquisition unit is used to extract the qualified group of steel bars. After the i-th longitudinal steel bar group, the j-th transverse steel bar group, and the k-th stirrup group waiting for concrete pouring are successfully matched with the positioning positions of each pouring component in the BIM model, steel bar embedding is carried out and pouring is prepared;

[0188] During the pouring process, the concrete pouring speed, vibration intensity during vibration, and stress at the contact points between the steel bars and the concrete of the q-th pouring component are monitored in real time, and then a stress distribution data set is established;

[0189] The second analysis unit is used to analyze and calculate according to the stress distribution data set to obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k .

[0190] The second analysis unit includes a longitudinal stress analysis unit, a transverse stress analysis unit, and a stirrup stress analysis unit;

[0191] The longitudinal stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group according to the stress distribution data set i , and the obtaining method is as follows:

[0192] S11. Extract the stress F at the m-th contact point of the i-th longitudinal steel bar group contact_i,mand the contact area A contact_i,m , after dimensionless processing, the stress value σ at the m-th contact point of the i-th longitudinal steel bar group is calculated by the following formula contact_i,m :

[0193]

[0194] S12. For the stress σ at the m-th contact point of the i-th longitudinal steel bar group contact_i,m The ratio of the average stress σ of all contact points contact_avg is weighted and averaged, combined with the concrete pouring speed V concrete and the influence value of the vibration intensity I of vibration vibration on the stress distribution. After dimensionless processing, the stress distribution coefficient zσ of the i-th longitudinal steel bar group is obtained i :

[0195]

[0196] In the formula, σ contact_avg is the average stress of all contact points, N m represents all contact points of the i-th longitudinal steel bar group; K1 and K2 are the influence coefficients of the concrete pouring speed V concrete and the vibration intensity I of vibration vibration respectively;

[0197] The transverse stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient hσ of the j-th transverse steel bar group according to the stress distribution data set j , and the obtaining method is as follows:

[0198] S21. Extract the stress F at the m-th contact point of the j-th transverse steel bar group contact_j,m and the contact area A contact_j,m , after dimensionless processing, the stress value σ at the m-th contact point of the j-th transverse steel bar group is calculated by the following formula contact_j,m :

[0199]

[0200] S22. For the ratio of the stress value σ at the m-th contact point of the j-th transverse steel bar group contact_j,m and the average stress σ of all contact points contact_avg , a weighted average is performed, combined with the concrete pouring speed V concrete and the influence value of the vibration intensity I of vibration vibration on the stress distribution. After dimensionless processing, the stress distribution coefficient hσ of the j-th transverse steel bar group is obtained j :

[0201]

[0202] Wherein, σ contact_avg is the average stress of all contact points, and M m represents all contact points of the j-th transverse steel bar group;

[0203] The stirrup stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient gσ of the k-th stirrup group according to the stress distribution data set k , and the obtaining method is as follows:

[0204] S31. Extract the stress F contact_k,m and contact area A contact_k,m of the m-th contact point of the k-th stirrup group. After dimensionless processing, the stress value σ of the m-th contact point of the k-th stirrup group is calculated through the following formula contact_k,m :

[0205]

[0206] S32. Perform weighted average on the ratio of the stress value σ contact_k,m of the m-th contact point of the k-th stirrup group and the average stress σ contact_avg of all contact points, and combine the influence values of the concrete pouring speed V concrete and the vibration intensity I vibration of the vibration on the stress distribution. After dimensionless processing, the stress distribution coefficient gσ of the k-th stirrup group is obtained k :

[0207]

[0208] Wherein, σ contact_avg is the average stress of all contact points, and G m represents all contact points of the k-th stirrup group.

[0209] In this embodiment, by monitoring the stress distribution of the contact points between the steel bars and the concrete, the stress conditions of the steel bar groups during the pouring process can be obtained in real time, ensuring the mutual matching of the concrete pouring and the steel bar binding quality, and avoiding structural strength problems caused by uneven stress. The calculation and analysis of the stress distribution coefficient help to reveal possible quality hazards and make timely adjustments. During the pouring process, the module can monitor key parameters such as the concrete pouring speed and the vibration intensity of the vibration in real time, and perform dynamic analysis in combination with the stress distribution data. This real-time feedback can timely detect potential problems, such as insufficient concrete fluidity or steel bar position deviation, and then take appropriate measures to ensure the smooth progress of the pouring process.

[0210] Through the stress analysis units for longitudinal, transverse, and stirrup groups, the system can analyze different types of steel bar groups separately, and finely evaluate the stress states of each steel bar group during the pouring process. This not only improves the accuracy of the analysis, but also provides more targeted adjustment strategies for various steel bar groups, optimizing the strength of the overall structure.

[0211] After the qualified steel bar groups are extracted, the system uses the BIM model to accurately match the positions of each steel bar group, ensuring that the embedded position of each steel bar is consistent with the design scheme. By combining with the BIM model, the construction accuracy during the pouring process is greatly improved, and problems such as construction errors or inaccurate steel bar positions are reduced.

[0212] Combining the influence of the concrete pouring speed and the vibration intensity on the stress distribution of the steel bar groups, the system can comprehensively consider multiple factors during the construction process, thereby accurately calculating the stress distribution coefficient of each steel bar group. This analysis method combining multiple factors provides a more detailed construction quality assessment, ensuring the safety and stability of the structure.

[0213] Example 7

[0214] This example is an explanatory note in Example 1. Please refer to Figure 1 , specifically, the second regulation module includes a second evaluation unit and a second control unit;

[0215] The second evaluation unit is used to preset the stress uniformity threshold Y, and the stress uniformity threshold Y includes the first stress uniformity threshold Y1, the second stress uniformity threshold Y2, and the third stress uniformity threshold Y3. Compare the stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k with the corresponding first stress uniformity threshold Y1, second stress uniformity threshold Y2, and third stress uniformity threshold Y3 respectively for comparative analysis to obtain the fourth evaluation result, including:

[0216] If zσ i ≥Y1, it means that the stress distribution uniformity of the i-th longitudinal steel bar group is qualified, and a qualified label is generated;

[0217] If zσ i <Y1, it means that the stress distribution uniformity of the i-th longitudinal steel bar group is unqualified, and the seventh unqualified alarm is triggered;

[0218] If hσ j ≥Y2, it means that the stress distribution uniformity of the j-th transverse steel bar group is qualified, and a qualified label is generated;

[0219] If hσ j<Y2 indicates that the stress distribution uniformity of the j-th transverse steel bar group is unqualified, triggering the eighth unqualified alarm;

[0220] If gσ k ≥Y3, it indicates that the stress distribution uniformity of the k-th stirrup group is qualified, generating a qualified label;

[0221] If gσ k <Y3, it indicates that the stress distribution uniformity of the k-th stirrup group is unqualified, triggering the ninth unqualified alarm.

[0222] The second control unit is used to generate corresponding control strategies based on the fourth evaluation result, including:

[0223] Identify the fourth evaluation result. For the stress distribution coefficient zσ of the i-th longitudinal steel bar group, the stress distribution coefficient hσ of the j-th transverse steel bar group i and the stress distribution coefficient gσ of the k-th stirrup group j k where the generated qualified labels are summarized into the construction qualified group; and receive the seventh unqualified alarm, the eighth unqualified alarm, and the ninth unqualified alarm triggered in the fourth evaluation result, and generate corresponding strategies, including:

[0224] According to the seventh unqualified alarm, generate the seventh strategy, including: adjusting the current concrete pouring speed to slow down by 3%-4%, and regulating the vibration intensity during vibration to be 5000-5500 times per minute, and increasing the vibration time for each layer of concrete to 15-20 seconds; and installing an additional 2%-3% steel mesh;

[0225] According to the eighth unqualified alarm, generate the seventh strategy, including: adjusting the current concrete pouring speed to slow down by 5%-6%, and regulating the vibration intensity during vibration to be 5600-6000 times per minute, and increasing the vibration time for each layer of concrete to 21-25 seconds; and installing an additional 4%-5% steel mesh;

[0226] According to the ninth unqualified alarm, generate the ninth strategy, including: adjusting the current concrete pouring speed to slow down by 7%-8%, and regulating the vibration intensity during vibration to be 6100-6500 times per minute, and increasing the vibration time for each layer of concrete to 26-30 seconds; and installing an additional 6%-7% steel mesh.

[0227] ​In this embodiment, by setting different stress uniformity thresholds (including the first, second, and third stress uniformity thresholds) and comparing the stress distribution coefficients of each steel bar group with them, the system can accurately evaluate the stress uniformity of the steel bar group. This refined evaluation method ensures that the distribution of steel bars in concrete meets the design requirements, avoiding problems such as local stress concentration or uneven distribution. When the stress distribution is unqualified, the system can automatically trigger unqualified alarms (the seventh, eighth, and ninth unqualified alarms). This real-time alarm mechanism helps construction personnel promptly discover potential quality problems and take targeted measures to avoid structural safety hazards.

[0228] According to the evaluation results, the system will automatically generate corresponding control strategies. These strategies include adjusting the concrete pouring speed, vibration intensity, vibration time of each layer of concrete, and increasing the proportion of the steel bar mesh. By intelligently adjusting these parameters, the system can effectively optimize the concrete pouring process, ensure a more uniform combination of steel bars and concrete, and enhance the stability of the overall structure.

[0229] The size of the threshold is set for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the number of base values set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0230] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent management system for construction quality and safety, characterized in that Including: The steel bar construction binding monitoring module is used to set detection points at the construction site. During the steel bar binding process, it collects the steel bar group binding data of several steel bar groups during the steel bar binding process, and based on the steel bar group binding data; Using a convolutional neural network, a steel bar group prediction model is constructed. After training the steel bar group prediction model with the steel bar group tying data, the tying quality coefficient zBz of the i-th longitudinal steel bar group, i the tying quality coefficient hBz of the j-th transverse steel bar group, j and the tying quality coefficient gBz of the k-th stirrup group k are analyzed and, after evaluation, the corresponding evaluation results are obtained; The first regulation module is used to identify the corresponding evaluation result. If it is identified as a qualified label, it outputs and summarizes it as a qualified steel bar group, waiting for concrete pouring; If it is identified as an unqualified alarm for the corresponding evaluation result, it generates the corresponding strategy and implements it; The casting dynamic detection module is used to continuously monitor the concrete casting of the steel bar combined grid group to establish a stress distribution data set, and analyze and obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k ; The second regulation module is used to evaluate the stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k to obtain the corresponding evaluation results and generate the corresponding strategies.

2. The intelligent management system for construction quality and safety according to claim 1, wherein The steel bar construction binding monitoring module includes a first collection unit and a first analysis unit; The first collection unit is used to set detection points at the construction site. First, it collects the steel bar group binding data of several steel bar groups during the steel bar binding process. According to the number of each steel bar group, it extracts the on-site binding parameters of the i-th longitudinal steel bar group, the j-th transverse steel bar group, and the k-th stirrup group; According to the on-site tying parameters, extract the number of turns of each tying point, establish a set of tying turns, and mark it as: {T loop,1 、T loop,2 、T loop,3 、...、T loop,y}, where T loop,1 to T loop,y represent the number of turns of the first tying point to the y-th tying point; By means of a binding device equipped with a force sensor, the tension data of the binding points is automatically recorded to establish a set of tension data, and the tension values of multiple binding points are marked as: {T tie,1 、T tie,2 、T tie,3 、...、T tie,y}, where T tie,1 to T tie,y represent the tension values of the first binding point to the y-th binding point.

3. The intelligent management system for construction quality and safety according to claim 2, wherein The first analysis unit is used to extract the binding point data of each steel bar group in the set of binding turns, and calculate and obtain the average steel bar spacing S through the following formula JJ , the average number of turns value T of the binding belt loop and the average tension T of the binding belt tie : Wherein, L is the length of the expected concrete component, Jr represents the end spacing, and N total represents the total number of steel bars in the steel bar group, d represents the diameter of the steel bar; L - 2Jr represents subtracting the spacing at both ends from the total length of the component, and -N total ×d represents subtracting the occupied space of each steel bar, specifically the product of the diameter of the steel bar and the total number; finally, divide by N total -1, because it is to calculate the spacing between two steel bars, so 1 should be subtracted to obtain the average spacing value; Where N represents the total number of binding points of the steel bar group, and T loop,y represents the number of turns of the y-th binding point; T tie,y represents the tension value of the y-th binding point; The binding data of the steel bar group includes: the diameter d of the steel bar f , the total number N of steel bars total , the average spacing S of the steel bars JJ , the average number of turns T of the binding tape loop and the average tension T of the binding tape tie .

4. The intelligent management system for construction quality and safety according to claim 3, characterized in that The steel bar construction binding monitoring module also includes a model establishment unit; The model building unit is used to build an initial convolutional neural network model using a convolutional neural network, train and test the initial convolutional neural network model with the steel bar group binding data, and use the trained initial convolutional neural network model as the steel bar group prediction model. At the same time, the intermediate layer output of the device operation state model is used as a feature vector to identify feature information, and the steel bar group prediction model is trained and tested with the obtained feature information. The trained steel bar group prediction model is used for data operation prediction to analyze and obtain the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th transverse steel bar group j and the binding quality coefficient gBz of the k-th stirrup group k ; The binding quality coefficient \(z_{Bz}\) of the \(i\)th longitudinal steel bar group i is calculated by the following formula: Among them, represents the cross-sectional area of a single reinforcing bar in the i-th longitudinal reinforcing bar group, which affects the load-bearing capacity of the reinforcing bar group, N total,i represents the total number of reinforcing bars in the i-th longitudinal reinforcing bar group, indicating the total load. The product of these two represents the load-bearing capacity of the entire longitudinal reinforcing bar; S JJ,i represents the average spacing of the reinforcing bars in the i-th longitudinal reinforcing bar group, which affects the uniformity of the force. The larger the spacing, the more dispersed the force; D tie,i represents the average spacing of the binding bands in the i-th longitudinal reinforcing bar group. The smaller the spacing, the tighter the binding and the higher the quality; T loop,i represents the average number of turns of the binding bands in the i-th longitudinal reinforcing bar group. The more the number of bindings, the better the stability; T tie,i represents the average tension of the binding bands in the i-th longitudinal reinforcing bar group; represents the tension term, taking 1 / 100 as the weight; The binding quality coefficient hBz of the j-th transverse steel bar group j is calculated by the following formula: Among them, The calculation logic is the same as that of the longitudinal reinforcement; represents the cross-sectional area of a single reinforcement bar in the j-th transverse reinforcement group, N total,j indicates the total number of reinforcement bars in the j-th transverse reinforcement group; (S JJ,j + D tie,j ) × T loop,j Different from the longitudinal direction, (S JJ.j + D tie,j ) is for addition instead of multiplication because the transverse steel bars are less stressed and their tying quality is more easily affected by the spacing. Therefore, directly adding the average spacing S JJ.j of the steel bars and the average spacing D tie,j of the tying bands in the j-th transverse steel bar group represents the combined effect of the two; T loop,j represents the average number of tying band loops in the j-th transverse steel bar group; the higher the number of tying band loops, the more tying times and the better the stability; T tie,j represents the average tension of the tying bands in the j-th transverse steel bar group; represents the tension term. Since the tying of the transverse steel bars is less sensitive to tension, the tension weight is reduced to 1 / 120; The binding quality coefficient gBz of the k-th stirrup group k is calculated by the following formula: Among them, The calculation logic is the same as that of longitudinal steel bars and transverse steel bars; represents the cross-sectional area of a single steel bar in the k-th stirrup group, N total,k represents the total number of steel bars in the k-th stirrup group; S JJ,k represents the average spacing of steel bars in the k-th stirrup group; D tie,k represents the average spacing of binding straps in the k-th stirrup group; T loop,k represents the average number of turns of binding straps in the k-th stirrup group; S JJ,k × D tie,k × (T loop,k + 1) is different from the horizontal and vertical directions. (T loop,k + 1) instead of T loop is for addition because stirrups are usually used in frame structures to bear large shear forces. The denser the binding, the higher the structural stability. Therefore, an extra 1 is added to emphasize the influence of the number of turns on the quality; T tie,k represents the average tension of the binding straps in the k-th stirrup group; represents the tension term. Since the binding quality of stirrups is more dependent on tension, the weight of the tension term is set to 1 / 90, which is more important than 1 / 120 for horizontal steel bars.

5. The intelligent management system for construction quality and safety according to claim 4, characterized in that The first regulation module includes a first evaluation unit and a first control unit; the first evaluation unit is configured to preset a first threshold X1, a second threshold X2, and a third threshold X3; and to compare the binding quality coefficient zBz of the i-th longitudinal steel bar group i , the binding quality coefficient hBz of the j-th transverse steel bar group j , and the binding quality coefficient gBz of the k-th stirrup group k with the first threshold X1, the second threshold X2, and the third threshold X3 respectively to determine whether the quality of the steel bar group meets the standard, including: The first threshold X1 includes a first passing threshold and a first maximum threshold Compare the binding quality coefficient zBz of the i-th longitudinal steel bar group i with the first threshold value X1 to obtain the first evaluation result, including: If indicates that the binding quality of the longitudinal steel bar group is unqualified and too loose, resulting in unqualified structural strength and triggering the first unqualified alarm; If It indicates that the binding quality of the longitudinal steel bar group is qualified, the binding is uniform and the strength meets the standard. It can not only meet the stability requirements of the structure, but also avoid the waste caused by excessive tightness, and generate a qualified label; If indicates that the binding quality of the longitudinal steel bar group is unqualified, too tight, there is a risk of wasting costs and it affects the pouring fluidity of concrete, triggering the second unqualified alarm; The second threshold X2 includes a second pass threshold and a second maximum threshold Compare the binding quality coefficient hBz of the j-th transverse steel bar group j with the second threshold value X2 to obtain a second evaluation result, including: If indicates that the binding quality of the horizontal steel bar group is unqualified and too loose, resulting in unqualified structural strength and triggering the third unqualified alarm; If It indicates that the binding quality of the horizontal steel bar group is qualified, the binding is uniform and the strength meets the standard, which can not only meet the stability requirements of the structure, but also avoid the waste of excessive tightness, and generate a qualified label; If indicates that the binding quality of the horizontal steel bar group is unqualified, too tight, there is a risk of cost waste and it affects the concrete construction, triggering the fourth unqualified alarm; The third threshold X3 includes a third passing threshold and a third maximum threshold The binding quality coefficient gBz of the k-th stirrup group k Compare with the third threshold X3 to obtain the third evaluation result, including: If indicates that the binding quality of the stirrup group is unqualified and too loose, resulting in unqualified structural strength and triggering the fifth unqualified alarm; If It indicates that the binding quality of the stirrup group is qualified, the binding is uniform and the strength meets the standard, which can not only meet the stability requirements of the structure, but also avoid the waste caused by excessive tightness, and generate a qualified label; If indicates that the binding quality of the stirrup group is unqualified, too tight, posing a risk of cost waste and affecting concrete construction, triggering the sixth unqualified alarm.

6. The intelligent management system for construction quality and safety according to claim 5, wherein The first control unit is configured to identify the first evaluation result, the second evaluation result, and the third evaluation result, and for the i-th longitudinal steel bar group with a qualified label, the binding quality coefficient zBz i , the j-th transverse steel bar group binding quality coefficient hBz j , and the k-th stirrup group binding quality coefficient gBz k , which are summarized into the qualified group of steel bars and await concrete pouring; According to the first unqualified alarm, generate the first strategy, including: reducing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group to increase the density; increasing the average number of turns of the binding belt by 1-2 turns to improve the overall tightness of the steel bars; reducing the average spacing of the binding belt by 3%-6% to increase the tightness of the binding belt; increasing the average tension of the binding belt by 5%-10% to enhance the fastening effect of the binding belt; According to the second unqualified alarm, generate the second strategy, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group, increasing the average spacing of the binding belt by 3%-6% to reduce the impact of excessive density; According to the third unqualified alarm, generate the third strategy, including: reducing the average spacing of 3%-6% of the steel bars in this transverse steel bar group, increasing the average number of turns of the binding belt by 1-2 turns, reducing the average spacing of the binding belt by 3%-6%, and increasing the average tension of the binding belt by 5%-10%; According to the fourth unqualified alarm, generate the fourth strategy, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group, increasing the average spacing of the binding belt by 3%-6%; According to the fifth unqualified alarm, generate the fifth strategy, including: reducing the average spacing of 3%-6% of the steel bars in this stirrup group, increasing the average number of turns of the binding belt by 1-2 turns, reducing the average spacing of the binding belt by 3%-6%, and increasing the average tension of the binding belt by 5%-10%; According to the sixth unqualified alarm, generate the sixth strategy, including: increasing the average spacing of 3%-6% of the steel bars in this longitudinal steel bar group, increasing the average spacing of the binding belt by 3%-6%.

7. The intelligent management system for construction quality and safety according to claim 6, characterized in that, The pouring dynamic monitoring module includes a second collection unit and a second analysis unit; The second collection unit is used to extract the qualified steel bar group. After successfully matching the i-th longitudinal steel bar group, the j-th transverse steel bar group, and the k-th stirrup group waiting for concrete pouring with the positioning positions of each pouring component in the BIM model, it conducts steel bar embedding and prepares for pouring; During the pouring process, it real-time monitors the concrete pouring speed, vibration intensity of the vibration, and the stress at the contact points between the steel bars and the concrete of the q-th pouring component, and then establishes a stress distribution data set; The second analysis unit is configured to analyze and calculate to obtain the stress distribution coefficient zσ of the ith longitudinal steel bar group based on the stress distribution data set i , the stress distribution coefficient hσ of the jth transverse steel bar group j and the stress distribution coefficient gσ of the kth stirrup group k .

8. The intelligent management system for construction quality and safety according to claim 7, characterized in that, The second analysis unit includes a longitudinal stress analysis unit, a transverse stress analysis unit, and a stirrup stress analysis unit; The longitudinal stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group according to the stress distribution data set i , and the obtaining method is as follows: S11. Extract the stress F of the m-th contact point of the i-th longitudinal steel bar group contact_i,m and the contact area A contact_i,m . After dimensionless processing, the stress value σ of the m-th contact point of the i-th longitudinal steel bar group is calculated through the following formula contact_i,m : S12. Calculate the ratio of the stress σ at the m-th contact point of the i-th longitudinal steel bar group to the average stress σ of all contact points, perform a weighted average, and combine it with the concrete pouring speed V comtact_i,m and the influence value of the vibration intensity I of the vibration on the stress distribution. After dimensionless processing, obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group contact_avg : concrete and the influence value of the vibration intensity I of the vibration on the stress distribution. After dimensionless processing, obtain the stress distribution coefficient zσ of the i-th longitudinal steel bar group vibration : i ​ where, σ contact_avg is the average stress of all contact points, N m represents all contact points of the i-th longitudinal steel bar group; k1 and K2 are the influence coefficients of the concrete pouring speed V concrete and the vibration intensity I of vibration vibratino respectively; The lateral stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient hσ of the j-th lateral steel bar group according to the stress distribution data set j , and the obtaining method is as follows: S21. Extract the stress F of the m-th contact point of the j-th transverse steel bar group contact_j,m and the contact area A contact_j,m . After dimensionless processing, the stress value σ of the m-th contact point of the j-th transverse steel bar group is calculated through the following formula contact_j,m : S22. Calculate the weighted average of the ratio of the stress value σ at the m-th contact point of the j-th transverse steel bar group contact_j,m to the average stress σ at all contact points contact_avg , and combine it with the concrete pouring speed V voncrete and the influence value of the vibration intensity I during vibration vibration on the stress distribution. After dimensionless processing, obtain the stress distribution coefficient hσ of the j-th transverse steel bar group j : where, σ contact_avg is the average stress of all contact points, and M m represents all contact points of the j-th transverse steel bar group; The stirrup stress analysis unit is used to deeply analyze and obtain the stress distribution coefficient gσ of the k-th stirrup group according to the stress distribution data set k , and the obtaining method is as follows: S31. Extract the stress F of the m-th contact point of the k-th stirrup group contact_k,m and the contact area A contact_k,m . After dimensionless processing, the stress value σ of the m-th contact point of the k-th stirrup group is calculated through the following formula contact_k,m : S32. Perform a weighted average on the ratio of the stress value σ at the m-th contact point of the k-th stirrup group contact_k,m to the average stress σ at all contact points contact_avg , and combine the concrete pouring speed V concrete and the vibration intensity I of vibration vibration for the influence value on the stress distribution. After dimensionless processing, obtain the stress distribution coefficient gσ of the k-th stirrup group k : where σ contact_avg is the average stress of all contact points, and G m represents all contact points of the k-th stirrup group.

9. The intelligent management system for building construction quality and safety according to claim 8, characterized in that The second regulation module includes a second evaluation unit and a second control unit; The second evaluation unit is used to preset a stress uniformity threshold Y, and the stress uniformity threshold Y includes a first stress uniformity threshold Y1, a second stress uniformity threshold Y2, and a third stress uniformity threshold Y3. The stress distribution coefficient zσ of the i-th longitudinal steel bar group i , the stress distribution coefficient hσ of the j-th transverse steel bar group j , and the stress distribution coefficient gσ of the k-th stirrup group k are respectively compared and analyzed with the corresponding first stress uniformity threshold Y1, second stress uniformity threshold Y2, and third stress uniformity threshold Y3 to obtain a fourth evaluation result, including: If zσ i ≥ Y1, it means that the stress distribution uniformity of the i-th longitudinal steel bar group is qualified, and a qualified label is generated; If zσ i <Y1, it means that the stress distribution uniformity of the i-th longitudinal steel bar group is unqualified, triggering the seventh unqualified alarm; If hσ j ≥ Y2, it indicates that the stress distribution uniformity of the j-th transverse steel bar group is qualified, and a qualified label is generated; If hσ j <Y2, it indicates that the stress distribution uniformity of the j-th transverse steel bar group is unqualified, triggering the eighth unqualified alarm; If gσ k ≥ Y3, it indicates that the stress distribution uniformity of the k-th stirrup group is qualified, and a qualified label is generated; If gσ k <Y3, it indicates that the stress distribution uniformity of the k-th stirrup group is unqualified, triggering the ninth unqualified alarm.

10. The intelligent management system for construction quality and safety according to claim 9, characterized in that The second control unit is configured to generate a corresponding control strategy according to the fourth evaluation result, including: Identify the fourth evaluation result, for the stress distribution coefficient zσ of the i-th longitudinal steel bar group that generates qualified labels i , the stress distribution coefficient hσ of the j-th transverse steel bar group j and the stress distribution coefficient gσ of the k-th stirrup group k , summarize them into the construction qualified group; and receive the seventh non-conformance alarm, the eighth non-conformance alarm, and the ninth non-conformance alarm triggered in the fourth evaluation result, and generate corresponding strategies, including: Generating a seventh strategy according to the seventh unqualified alarm, including: adjusting the current concrete pouring speed to be slowed down by 3%-4%, regulating the vibration intensity during vibration to be 5000-5500 times per minute, increasing the vibration time for each layer of concrete to 15-20 seconds; and adding a steel mesh with an increase of 2%-3%; Generating an eighth strategy according to the eighth unqualified alarm, including: adjusting the current concrete pouring speed to be slowed down by 5%-6%, regulating the vibration intensity during vibration to be 5600-6000 times per minute, increasing the vibration time for each layer of concrete to 21-25 seconds; And adding a steel mesh with an increase of 4%-5%; Generating a ninth strategy according to the ninth unqualified alarm, including: adjusting the current concrete pouring speed to be slowed down by 7%-8%, regulating the vibration intensity during vibration to be 6100-6500 times per minute, increasing the vibration time for each layer of concrete to 26-30 seconds; And adding a steel mesh with an increase of 6%-7%.

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