Building supervision quality management detection method and system

Abstract

The invention relates to the technical field of data analysis, in particular to a building supervision quality management detection method and system, and the method comprises the steps: carrying out the real-time data collection of the construction progress of building engineering, obtaining engineering parameters, carrying out the data processing of the engineering parameters, and determining the multi-dimensional feature indexes of the quality management detection method; calculating dynamic weights of the multi-dimensional characteristic indexes through a dynamic weight model; performing normalization processing on the feature indexes to obtain normalization scores of the multi-dimensional feature indexes; and fusing the multi-dimensional characteristic parameters by using the comprehensive quality evaluation model to obtain quality evaluation parameters, and performing data analysis on the quality evaluation parameters to determine a quality management detection method. According to the method, the dynamic weight of the multi-dimensional characteristic index is calculated through the dynamic weight model, the subjectivity of a fixed weight is avoided, and the requirements of different construction stages are met; the building structure is fully covered in structure, material, process and environment, and management blind areas are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a method and system for building supervision quality management and detection. Background Art

[0002] Building supervision quality management is a systematic work that ensures that the entire project cycle from design, construction to completion meets safety standards, performance requirements and regulatory constraints through technical specifications, process control and supervision means. Quality inspection relies on sensors, laboratory tests and digital tools to quantitatively verify key indicators such as material strength, structural stability, and process compliance, which is the core implementation link of quality management.

[0003] In the existing quality management and detection methods, it is difficult to dynamically adjust the evaluation weights based on multi-dimensional data of building structures according to the construction stage, resulting in the evaluation results being unable to meet the needs of different construction stages. At the same time, there are blind spots in different dimensions in the data evaluation of building projects, such as the process and environmental adaptation levels. This makes it difficult for the building links to fully and real-time grasp the quality status, resulting in most of the treatment methods for construction projects being based on post-event rectification, which is not only inefficient but also prone to increasing the rework rate and the effect is not good. Summary of the Invention

[0004] The object of the present invention is to propose a method and system for building supervision quality management and detection in view of the problems existing in the background art.

[0005] The technical solution of the present invention: A method for building supervision quality management and detection includes the following steps:

[0006] S1. Deploy a sensor network and a storage terminal, collect real-time data on the construction progress of a building project, and update it to the storage terminal in real time. Obtain the project parameters synchronized in real time by the sensor network and the Internet of Things terminal, process the project parameters, and determine the structural stability parameters, material strength parameters, process compliance parameters and environmental adaptation parameters of the building structure, and use them as multi-dimensional characteristic indicators for determining the quality management and detection method;

[0007] S2. Calculate the dynamic weights of the multi-dimensional characteristic indicators through a dynamic weight model to avoid the subjectivity of fixed weights and meet the needs of different construction stages;

[0008] S3. Normalize the characteristic indicators to obtain the normalized scores of the multi-dimensional characteristic indicators;

[0009] S4. Based on the normalized scores and dynamic weights of the multi-dimensional characteristic indicators, use a comprehensive quality evaluation model to fuse the multi-dimensional characteristic parameters, obtain quality evaluation parameters that take into account the normalized scores and dynamic weights of the multi-dimensional characteristic indicators, and perform data analysis on the quality evaluation parameters to determine the quality management and detection method.

[0010] Preferably, for S1, the method for processing engineering parameters includes:

[0011] Calculating the structural stability parameters of the building structure through a stability evaluation formula, and the stability evaluation formula is as follows:

[0012]

[0013] In the formula, ΔD(t) is the structural stability parameter of the building structure at time t; d i (t) is the real-time displacement value of the building materials in the x, y, and z directions at time t; d i，初始 is the initial reference value calibrated before the construction of the building structure; i is the numbering of the x, y, and z directions.

[0014] Preferably, for S1, the method for processing engineering parameters further includes:

[0015] Calculating the material strength parameters of the building structure through a material strength evaluation formula, and the material strength evaluation formula is as follows:

[0016]

[0017] In the formula, M(t) is the material strength parameter of the building structure at time t; T t is the real-time temperature of the building structure at time t; T0 is the reference temperature of the building structure; Δt is the sampling interval.

[0018] Preferably, for S1, the method for processing engineering parameters further includes:

[0019] Determining the process compliance parameters of the building structure, and the method is as follows:

[0020] Obtaining and identifying the building engineering plan, determining the preset construction requirements and construction processes specified in the building engineering plan, and counting the number of processes in the specified construction process to obtain the total number of specified construction processes;

[0021] Obtaining the construction image of the construction site based on computer vision technology, identifying the construction image through the YOLO algorithm, determining the current real-time construction process, and judging whether the actions and tools used by the workers meet the specified operation requirements. If so, marking the real-time construction process as the correct process; counting the total number of correct processes;

[0022] Calculating the stability parameter through the formula to obtain;

[0023] In the formula, P(t) is the process compliance parameter of the building structure based on time t; P true is the total number of correct processes; P totalFor the total number of standardized construction processes.

[0024] Preferably, for S1, the method for data processing of engineering parameters further includes:

[0025] Determine the environmental adaptation parameters of the building structure, the method is as follows:

[0026] Take the real-time temperature and real-time humidity as two target parameters respectively, and sequentially obtain the mean value of the target parameters within the past 1 hour of the building structure, and the standard deviation of the target parameters within the past 1 hour;

[0027] Perform data analysis on the target parameters of the building structure at time t. If the target parameters meet Then mark the target parameter as an abnormal parameter and eliminate the abnormal parameter; In the formula, Z(j) t Is the value of the target parameter at time t; Is the mean value of the target parameter; σ(j) is the standard deviation of the target parameter; j is the target parameter number, j ∈ (1, 2); where, j = 1 represents the real-time temperature; j = 2 represents the real-time humidity;

[0028] For the loss of temperature signal caused by eliminating abnormal parameters, use the linear interpolation algorithm to complement the target parameters;

[0029] Through the formula Calculate to obtain the environmental adaptation parameters;

[0030] In the formula, I E (t) is the environmental adaptation parameter of the building structure at time t; α1 and α2 are the attenuation coefficients of the real-time temperature and real-time humidity respectively; T opt Is the optimal curing temperature of the building material; H opt The optimal curing humidity of the building material.

[0031] Preferably, for S2, the method for calculating the dynamic weight of multi-dimensional feature indicators through a dynamic weight model is:

[0032] Establish a sliding window period for the feature indicators, count the amount of data in the sliding window period and mark it as Nh; h is the feature indicator number, h = [1, n], n is the total number of feature indicators;

[0033] Through the formula Calculate to obtain the entropy value of the feature indicator based on the sliding window period at time t;

[0034] In the formula, Pik h (t) is the proportion of the kth sample value of the feature indicator within the sliding window;

[0035] Through the formula Calculate to obtain the dynamic weight W of the feature indicator at time th (t);

[0036] Wherein, d is the characteristic index number, h = [1, n], and n is the total number of characteristic indexes;

[0037] Through the formula The entropy value of the characteristic index based on the sliding window period at time t is calculated;

[0038] Wherein, Pik h (t) is the proportion of the k-th sample value of the characteristic index in the sliding window;

[0039] Through the formula The dynamic weight W of the characteristic index at time t is calculated h (t);

[0040] Wherein, d is the characteristic index number, h = [1, n], and n is the total number of characteristic indexes.

[0041] Preferably, for S3, the normalization process of the characteristic index includes the following methods:

[0042] S31, normalize the structural stability parameters, and the method is as follows:

[0043] Through the formula The structural stability score I1(t) is calculated;

[0044] Wherein, D max Is the maximum displacement threshold allowed by the design;

[0045] S32, normalize the material strength parameters, and the method is as follows:

[0046] The real-time strength of the building structure is calculated by the formula f(t) = a·M(t) + b;

[0047] Wherein, both a and b are material test calibration coefficients;

[0048] Through the minimum value comparison function The material strength score I2(t) is calculated; wherein, f design Is the design strength requirement;

[0049] Exemplarily, if the material strength score I2(t) < 0.9, automatically adjust the curing plan, such as starting spraying or heating;

[0050] S33, take the process compliance parameter and the environmental adaptability parameter as the process compliance score I3(t) and the environmental adaptability score I4(t) respectively.

[0051] Preferably, for S4, the expression of the comprehensive quality evaluation model is:

[0052] Calculate the quality assessment parameters of the building structure at time t;

[0053] Where, I h (t) is the score corresponding to the characteristic index; δ m (t) is the abnormal fluctuation function of the m-th characteristic index; λ is the penalty intensity coefficient;

[0054] The expression of the abnormal fluctuation function is:

[0055] Where, μ h (t) is the mean value of the characteristic index in the sliding window period, γ h (t) is the standard deviation of the characteristic index in the sliding window period.

[0056] Preferably, for S4, perform data analysis on the quality assessment parameters to determine the quality management detection method as follows:

[0057] Perform real-time monitoring on the quality assessment parameter Q total , and compare the quality assessment parameter Q total with the preset first quality assessment threshold Q Fth and the second quality assessment threshold Q Sth in sequence;

[0058] If Q total > Q Fth , it is determined that the building structure is normal, and continue to monitor the building structure;

[0059] If Q Sth ≤Q total <Q Fth , it is determined that the building structure is slightly abnormal, give an early warning, and inform the technical personnel to conduct manual review;

[0060] If Q total < Q Sth , it is determined that the building structure is seriously abnormal, give an alarm, and stop work urgently.

[0061] The present invention also discloses a building supervision quality management detection system, which applies the above-mentioned building supervision quality management detection method, and specifically includes:

[0062] A data acquisition and processing module, used to deploy a sensor network and a storage terminal, perform real-time data acquisition on the construction progress of the building project, and update it to the storage terminal in real time, obtain the project parameters synchronously in real time by the sensor network and the Internet of Things terminal, perform data processing on the project parameters, determine the structural stability parameters, material strength parameters, process compliance parameters and environmental adaptability parameters of the building structure, and use them as multi-dimensional characteristic indicators for determining the quality management detection method;

[0063] A dynamic weight calculation module, which is used to calculate the dynamic weights of multi-dimensional feature indicators through a dynamic weight model, avoid the subjectivity of fixed weights, and adapt to the requirements of different construction stages;

[0064] A data analysis module, which is used to normalize the feature indicators to obtain the normalized scores of multi-dimensional feature indicators;

[0065] A quality management detection module, which is used to fuse multi-dimensional feature parameters based on the normalized scores and dynamic weights of multi-dimensional feature indicators by using a comprehensive quality evaluation model, obtain quality evaluation parameters that take into account the normalized scores and dynamic weights of multi-dimensional feature indicators, and perform data analysis on the quality evaluation parameters to determine the quality management detection method.

[0066] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0067] (1) By processing the engineering parameters, determine the structural stability parameters, material strength parameters, process compliance parameters, and environmental adaptation parameters of the building structure, and use them as multi-dimensional feature indicators for determining the quality management detection method. Calculate the dynamic weights of multi-dimensional feature indicators through a dynamic weight model, avoid the subjectivity of fixed weights, and adapt to the requirements of different construction stages.

[0068] (2) By normalizing the feature indicators, obtain the normalized scores of multi-dimensional feature indicators, and use a comprehensive quality evaluation model to fuse multi-dimensional feature parameters to obtain quality evaluation parameters that take into account the normalized scores and dynamic weights of multi-dimensional feature indicators, realizing full coverage of the building structure in terms of structure, materials, processes, and environment, and reducing management blind spots; through this method, project managers can grasp the quality status in real time, transform the traditional engineering processing means based on post-event rectification into a processing method based on pre-event prevention, and significantly reduce the rework cost. Description of the Drawings

[0069] Figure 1 It is the flowchart of the method of Embodiment 1 proposed by the present invention. Detailed Embodiment

[0070] Embodiment 1, as Figure 1 shown, a building supervision quality management detection method proposed by the present invention includes the following steps:

[0071] S1. Deploy a sensor network and a storage terminal to collect real-time data on the construction progress of a construction project and update it to the storage terminal in real time. Obtain the engineering parameters synchronized in real time by the sensor network and the Internet of Things terminal, process the engineering parameters, and determine the structural stability parameters, material strength parameters, process compliance parameters, and environmental adaptation parameters of the building structure, which are used as multi-dimensional characteristic indicators for determining the quality management detection method.

[0072] For S1, the method for processing the engineering parameters includes:

[0073] S11. Calculate the structural stability parameters of the building structure through a stability evaluation formula, and the stability evaluation formula is as follows:

[0074]

[0075] In the formula, ΔD(t) is the structural stability parameter of the building structure at time t; d i (t) is the real-time displacement value of the building material in the x, y, and z directions at time t; d i，初始 is the initial reference value calibrated before the construction of the building structure; i is the number of the x, y, and z directions;

[0076] It should be noted that the real-time displacement value d i (t) of the building material in the x, y, and z directions at time t is collected by a laser displacement sensor;

[0077] Exemplarily, when the structural stability parameter ΔD(t)>0.8D max , a secondary warning is triggered; when it exceeds D max , the construction is automatically suspended and the engineer is notified;

[0078] S12. Calculate the material strength parameters of the building structure through a material strength evaluation formula, and the material strength evaluation formula is as follows:

[0079]

[0080] In the formula, M(t) is the material strength parameter of the building structure at time t; T t is the real-time temperature of the building structure at time t; T0 is the reference temperature of the building structure; Δt is the sampling interval; it should be noted that the real-time temperature T t is collected by a temperature sensor; the reference temperature T0 is based on the ASTM C1074 standard and is usually taken as -10°C;

[0081] S13. The method for determining the process compliance parameters of the building structure is as follows:

[0082] Obtain and identify the construction project plan, determine the preset construction requirements and construction processes specified in the construction project plan, and count the number of processes in the specified construction process to obtain the total number of specified construction processes;

[0083] Based on computer vision technology, obtain the construction images at the construction site, identify the construction images through the YOLO algorithm, determine the current real-time construction process, and judge whether the actions and tools used by the workers meet the specified operation requirements. If so, mark the real-time construction process as the correct process; count the total number of correct processes;

[0084] Through the formula Calculate to obtain the stability parameter;

[0085] In the formula, P(t) is the process compliance parameter of the building structure based on the moment t; P true is the total number of correct processes; P total is the total number of specified construction processes;

[0086] Exemplarily, if key steps, such as setting up the circuit, are not executed in sequence, the system locks the subsequent processes and pushes a rectification instruction;

[0087] S14, the method for determining the environmental adaptation parameter of the building structure is as follows:

[0088] Take the real-time temperature and real-time humidity as two target parameters respectively, and sequentially obtain the average value of the target parameters within the past 1 hour of the building structure, and the standard deviation of the target parameters within the past 1 hour;

[0089] Conduct data analysis on the target parameters of the building structure at time t. If the target parameters meet then mark the target parameters as abnormal parameters and eliminate the abnormal parameters; in the formula, Z(j) t is the value of the target parameter at time t; is the average value of the target parameter; σ(j) is the standard deviation of the target parameter; j is the target parameter number, j ∈ (1, 2); where, j = 1 represents the real-time temperature; j = 2 represents the real-time humidity; it should be noted that the real-time temperature is collected through a temperature sensor, and the real-time humidity is collected through a humidity sensor;

[0090] For the loss of temperature signals caused by the elimination of abnormal parameters, use the linear interpolation algorithm to complement the target parameters;

[0091] Through the formula Calculate to obtain the environmental adaptation parameter;

[0092] In the formula, I E (t) is the environmental adaptation parameter of the building structure at time t; α1 and α2 are the attenuation coefficients of the real-time temperature and real-time humidity respectively; Topt is the optimal curing temperature for building materials; H opt is the optimal curing humidity for building materials;

[0093] Exemplarily, when I E (t) < 0.7, the curing equipment, such as the spray system and heating blanket, is automatically started to adjust the environment;

[0094] S2. Calculate the dynamic weights of multi-dimensional characteristic indexes through a dynamic weight model, avoid the subjectivity of fixed weights, and adapt to the requirements of different construction stages;

[0095] For S2, the method of calculating the dynamic weights of multi-dimensional characteristic indexes through a dynamic weight model is as follows:

[0096] Establish a sliding window period for the characteristic indexes, count the data volume in the sliding window period and mark it as Nh; h is the characteristic index number, h = [1, n], and n is the total number of characteristic indexes;

[0097] Through the formula calculate the entropy value of the characteristic index at time t based on the sliding window period;

[0098] In the formula, Pik h (t) is the proportion of the k-th sample value of the characteristic index in the sliding window;

[0099] Through the formula calculate the dynamic weight W h (t);

[0100] In the formula, d is the characteristic index number, h = [1, n], and n is the total number of characteristic indexes;

[0101] S3. Normalize the characteristic indexes to obtain the normalized scores of multi-dimensional characteristic indexes;

[0102] For S3, the normalization of the characteristic indexes includes the following methods:

[0103] S31. Normalize the structural stability parameters, and the method is as follows:

[0104] Through the formula calculate the structural stability score I1(t);

[0105] In the formula, D max is the maximum displacement threshold allowed by the design; it should be noted that the maximum displacement threshold D max is based on structural design codes such as the Chinese "Code for Design of Building Structures" GB 50009;

[0106] S32. Normalize the material strength parameters, and the method is as follows:

[0107] The real-time strength of the building structure is calculated by the formula f(t) = a·M(t) + b;

[0108] Wherein, both a and b are calibration coefficients of material tests, based on the pressure test data of standard specimens cured synchronously;

[0109] Through the minimum value comparison function The material strength score I2(t) is calculated;

[0110] Wherein, f design is the design strength requirement; It should be noted that f design is based on design specifications such as the "Unified Standard for Reliability Design of Building Structures" (GB 50068);

[0111] S33, the process compliance parameter and the environmental adaptability parameter are respectively used as the process compliance score I3(t) and the environmental adaptability score I4(t);

[0112] S4. Based on the normalized score and dynamic weight of multi-dimensional characteristic indicators, the comprehensive quality assessment model is used to fuse the multi-dimensional characteristic parameters, and the quality assessment parameters considering the normalized score and dynamic weight of multi-dimensional characteristic indicators are obtained, and data analysis is carried out on the quality assessment parameters to determine the quality management detection method;

[0113] For S4, the expression of the comprehensive quality assessment model is:

[0114]

[0115] The quality assessment parameter of the building structure at time t is calculated;

[0116] Wherein, I h (t) is the score corresponding to the characteristic indicator; δ m (t) is the abnormal fluctuation function of the m-th characteristic indicator; λ is the penalty intensity coefficient; It should be noted that the penalty intensity coefficient λ is calibrated through historical big data;

[0117] The expression of the abnormal fluctuation function is:

[0118] Wherein, μ h (t) is the mean value of the characteristic indicator in the sliding window period, and γ h (t) is the standard deviation of the characteristic indicator in the sliding window period;

[0119] It should be noted that the formula part in the formula is the penalty term, and its function is to trigger the penalty mechanism and reduce the total score when the score of a certain parameter deviates from the historical mean by more than 1 standard deviation;

[0120] For S4, data analysis is performed on the quality assessment parameters to determine the quality management detection method as follows:

[0121] Perform real-time monitoring on the quality assessment parameter Q total and compare the quality assessment parameter Q total with the preset first quality assessment threshold Q Fth and the second quality assessment threshold Q Sth sequentially;

[0122] If Q total >Q Fth , it is determined that the building structure is normal, and the monitoring of the building structure continues;

[0123] If Q Sth ≤Q total <Q Fth , it is determined that the building structure is slightly abnormal, a warning is issued, and the technical personnel are informed to conduct manual review;

[0124] If Q total <Q Sth , it is determined that the building structure is severely abnormal, an alarm is issued, and the work is stopped urgently.

[0125] Embodiment 2. A building supervision quality management detection system proposed by the present invention is applied to a building supervision quality management detection method proposed in Embodiment 1, and specifically includes:

[0126] A data acquisition and processing module, which is used to deploy a sensor network and a storage terminal, perform real-time data acquisition on the construction progress of a building project, and update it to the storage terminal in real time, obtain engineering parameters synchronously in real time by the sensor network and the Internet of Things terminal, perform data processing on the engineering parameters, and determine the structural stability parameter, material strength parameter, process compliance parameter, and environmental adaptation parameter of the building structure, and use them as multi-dimensional characteristic indicators for determining the quality management detection method;

[0127] A dynamic weight calculation module, which is used to calculate the dynamic weights of the multi-dimensional characteristic indicators through a dynamic weight model, avoid the subjectivity of fixed weights, and adapt to the requirements of different construction stages;

[0128] A data analysis module, which is used to perform normalization processing on the characteristic indicators to obtain the normalized scores of the multi-dimensional characteristic indicators;

[0129] A quality management detection module, which is used to fuse the multi-dimensional characteristic parameters based on the normalized scores and dynamic weights of the multi-dimensional characteristic indicators, use a comprehensive quality assessment model to obtain a quality assessment parameter that takes into account the normalized scores and dynamic weights of the multi-dimensional characteristic indicators, and perform data analysis on the quality assessment parameter to determine the quality management detection method.

[0130] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited thereto, and various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those skilled in the art.

Claims

1. A construction supervision quality management detection method, characterized in that: The following steps are involved: S1. Deploy sensor networks and storage terminals to collect real-time data on the construction progress of the building project, and update it to the storage terminal in real time, obtain the engineering parameters synchronized in real time by the sensor network and the IoT terminal, process the engineering parameters, determine the structural stability parameters, material strength parameters, process compliance parameters and environmental adaptability parameters of the building structure, and use them as multi-dimensional characteristic indicators to determine the quality management detection method; S2. Calculate the dynamic weights of multi-dimensional characteristic indicators through a dynamic weight model to avoid the subjectivity of fixed weights and adapt to the needs of different construction stages; S3, normalizing the feature indicators to obtain normalized scores of the multidimensional feature indicators; S4. Based on the normalized scores and dynamic weights of multidimensional feature indicators, the comprehensive quality assessment model is used to fuse the multidimensional feature parameters to obtain quality assessment parameters that take into account the normalized scores and dynamic weights of multidimensional feature indicators. Data analysis is performed on the quality assessment parameters to determine the quality management detection method.

2. A construction supervision quality management detection method according to claim 1, characterized in that: For S1, the methods for data processing of engineering parameters include: The structural stability parameters of the building structure are calculated using the stability evaluation formula. The stability evaluation formula is as follows: Where ΔD(t) is the structural stability parameter of the building structure at time t; d i (t) is the real-time displacement value of the building material in the x, y, and z directions at time t; d i，初始 It is the initial reference value calibrated before the construction of the building structure; i is the number of the three directions x, y, and z.

3. A construction supervision quality management detection method according to claim 2, characterized in that: For S1, the method for data processing of engineering parameters also includes: The material strength parameters of the building structure are calculated using the material strength evaluation formula. The material strength evaluation formula is as follows: Where M(t) is the material strength parameter of the building structure at time t; T t is the real-time temperature of the building structure at time t; T0 is the reference temperature of the building structure; Δt is the sampling interval.

4. A construction supervision quality management detection method according to claim 3, characterized in that: For S1, the method for data processing of engineering parameters also includes: Determine the process compliance parameters of the building structure as follows: Obtain and identify the construction project plan, determine the standard construction requirements and standard construction processes preset in the construction project plan, and count the number of processes in the standard construction process to obtain the total number of standard construction processes; Based on computer vision technology, the construction images of the construction site are obtained, and the YOLO algorithm is used to identify the construction images to determine the current real-time construction process and judge whether the workers' actions and tools meet the standard operation requirements. If they do, the real-time construction process is marked as the correct process; the total number of correct processes is counted; By formula The stability parameters are calculated; Where P(t) is the process compliance parameter of the building structure at time t; P true is the total number of correct processes; P total To standardize the total number of construction processes.

5. A construction supervision quality management and detection method according to claim 4, characterized in that: For S1, the method for data processing of engineering parameters also includes: Determine the environmental adaptability parameters of the building structure as follows: The real-time temperature and real-time humidity are respectively used as two target parameters, and the mean value of the target parameters of the building structure in the past hour and the standard deviation of the target parameters in the past hour are obtained in turn; Perform data analysis on the target parameters of the building structure at time t. If the target parameters meet Then the target parameter is marked as an abnormal parameter and the abnormal parameter is eliminated; where Z(j) t is the value of the target parameter at time t; is the mean of the target parameter; σ(j) is the standard deviation of the target parameter; j is the target parameter number, j∈(1,2); where j=1 represents the real-time temperature; j=2 represents the real-time humidity; For the temperature signal loss caused by removing abnormal parameters, a linear difference algorithm is used to complete the target parameters; By formula Calculate the environmental adaptation parameters; In the formula, I E (t) is the environmental adaptation parameter of the building structure at time t; α1 and α2 are the attenuation coefficients of real-time temperature and real-time humidity respectively; T opt is the best curing temperature for building materials; opt Optimal curing humidity for building materials.

6. A construction supervision quality management and detection method according to claim 5, characterized in that: For S2, the method of calculating the dynamic weight of the multi-dimensional feature index through the dynamic weight model is: Establish a sliding window period of the characteristic index, count the amount of data in the sliding window period and mark it as Nh; h is the characteristic index number, h = [1, n], n is the total number of characteristic indicators; By formula The characteristic index is calculated based on the entropy value of the sliding window period at time t; In the formula, Pik h (t) is the proportion of the kth sample value of the feature index in the sliding window; By formula Calculate the dynamic weight W of the characteristic index at time t h (t); Where d is the characteristic index number, h = [1, n], and n is the total number of characteristic indexes.

7. A construction supervision quality management and detection method according to claim 3, characterized in that: For S3, the normalization process of feature indicators includes the following methods: S31, normalize the structural stability parameters as follows: By formula The structural stability score I1(t) is calculated; Where D max is the maximum displacement threshold allowed by the design; S32, normalize the material strength parameters as follows: The real-time strength of the building structure is calculated by the formula f(t)=a·M(t)+b; Where a and b are the material test calibration coefficients; Compare functions by minimum value The material strength score I2(t) is calculated; In the formula, f design Design strength requirements; S33, taking the process compliance parameter and the environmental adaptability parameter as the process compliance score I3(t) and the environmental adaptability score I4(t), respectively.

8. A construction supervision quality management and detection method according to claim 7, characterized in that: For S4, the expression of the comprehensive quality assessment model is: Calculate and obtain the quality assessment parameters of the building structure at time t; In the formula, I h (t) is the score corresponding to the characteristic index; δ m (t) is the abnormal fluctuation function of the mth characteristic index; λ is the penalty intensity coefficient; The expression of the abnormal fluctuation function is: In the formula, μ h (t) is the mean value of the characteristic index in the sliding window period, γ h (t) is the standard deviation of the characteristic index in the sliding window period.

9. A construction supervision quality management and detection method according to claim 8, characterized in that: For S4, data analysis of quality assessment parameters was performed to determine the quality management test methods as follows: The quality assessment parameter Q total Real-time monitoring is performed to determine the quality assessment parameter Q total The preset quality assessment first threshold Q Fth and the second quality assessment threshold Q Sth Compare them one by one; If Q total >Q Fth , the building structure is judged to be normal and the building structure continues to be monitored; If Q Sth ≤Q total <Q Fth , the building structure is judged to be slightly abnormal, an early warning is issued, and the technical staff is informed to conduct manual review; If Q total Sth , the building structure is judged to be seriously abnormal, an alarm is issued, and emergency work is stopped.​ 10. A construction supervision quality management detection system, applying a construction supervision quality management detection method according to any one of claims 1 to 9, characterized in that: Specifically include: The data collection and processing module is used to deploy sensor networks and storage terminals, collect real-time data on the construction progress of the building project, and update it to the storage terminal in real time, obtain the engineering parameters synchronized in real time by the sensor network and the Internet of Things terminal, process the engineering parameters, determine the structural stability parameters, material strength parameters, process compliance parameters and environmental adaptability parameters of the building structure, and use them as multi-dimensional characteristic indicators to determine the quality management detection method; Dynamic weight calculation module, used to calculate the dynamic weights of multi-dimensional feature indicators through a dynamic weight model, avoiding the subjectivity of fixed weights and adapting to the needs of different construction stages; The data analysis module is used to normalize the feature indicators and obtain the normalized scores of the multi-dimensional feature indicators; The quality management detection module is used to fuse the multidimensional feature parameters based on the normalized scores and dynamic weights of the multidimensional feature indicators using a comprehensive quality assessment model to obtain quality assessment parameters that take into account the normalized scores and dynamic weights of the multidimensional feature indicators, perform data analysis on the quality assessment parameters, and determine the quality management detection method.

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