A building supervision quality management detection method and system

Abstract

The application relates to the technical field of data analysis, in particular to a building supervision quality management detection method and system, which comprises the following steps: collecting real-time data of the construction progress of a building project, obtaining engineering parameters, processing the engineering parameters, determining the multi-dimensional characteristic indexes of the quality management detection method, calculating the dynamic weight of the multi-dimensional characteristic indexes through a dynamic weight model, normalizing the characteristic indexes, obtaining the normalized scores of the multi-dimensional characteristic indexes, fusing the multi-dimensional characteristic parameters by using a comprehensive quality evaluation model, obtaining quality evaluation parameters, analyzing the quality evaluation parameters, and determining the quality management detection method. The dynamic weight model is used to calculate the dynamic weight of the multi-dimensional characteristic indexes, the subjectivity of fixed weight is avoided, and the demand of different construction stages is met; the application also realizes the full coverage of the building structure in the aspects of structure, material, process and environment, and reduces the management blind area.

Description

TECHNICAL FIELD

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

[0002] Building supervision quality management is a systematic work through technical specifications, process control and supervision means to ensure that the project meets safety standards, performance requirements and regulatory constraints throughout the design, construction and completion cycle. Quality detection relies on sensors, laboratory testing and digital tools to quantify and verify key indicators such as material strength, structural stability and process compliance, and is the core execution link of quality management.

[0003] However, in the existing quality management detection method, the evaluation weight based on the multi-dimensional data of the building structure is difficult to dynamically adjust according to the construction stage, resulting in evaluation results that cannot adapt to the needs of different construction stages. At the same time, in the data evaluation aspect of the construction project, there are blind spots in different dimensions, such as process and environmental adaptation, which makes it difficult to completely and real-time grasp the quality state of the construction link, resulting in a processing method for the construction project that is mostly based on post-repair, which not only is inefficient but also easily increases the rework rate and has poor effect. SUMMARY

[0004] The present application aims to solve the problems in the background art and provides a building supervision quality management detection method and system.

[0005] The technical scheme of the present application is a building supervision quality management detection method, comprising the following steps:

[0006] S1, deploy a sensor network and a storage terminal, collect real-time data of the construction progress of the building project, and update the data to the storage terminal in real time to obtain the real-time synchronization of the engineering parameters by the sensor network and the Internet of Things terminal, process the data of 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;

[0007] S2, calculate the dynamic weight of the multi-dimensional feature indicators through a dynamic weight model to avoid the subjectivity of fixed weight and adapt to the needs of different construction stages;

[0008] S3, normalize the feature indicators to obtain the normalized scores of the multi-dimensional feature indicators;

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

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

[0011] The structural stability parameters of a building structure are calculated using a stability assessment formula, which 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) represents the real-time displacement values ​​of the building materials in the x, y, and z directions at time t; d i，初始 The initial reference value is determined before the construction of the building structure; i is the number of the three directions x, y, and z.

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

[0015] The material strength parameters of a building structure are calculated using a material strength assessment formula, which is as follows:

[0016]

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

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

[0019] The following methods are used to determine the process compliance parameters for building structures:

[0020] Acquire and identify building engineering plans, determine the pre-set standard construction requirements and standard construction processes in the building engineering plans, and count the number of standard construction processes to obtain the total number of standard construction processes.

[0021] Construction images of the construction site are acquired using computer vision technology. The YOLO algorithm is used to identify the construction images, determine the current real-time construction process, and judge whether the workers' actions and tools used meet the standard operating requirements. If they do, the real-time construction process is marked as the correct process; the total number of correct processes is counted.

[0022] Through formula The stability parameters were calculated.

[0023] In the formula, P(t) represents the process compliance parameters of the building structure based on time t; P true P represents the total number of correct processes. totalTo standardize the total number of construction processes.

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

[0025] The environmental adaptability parameters of a building structure are determined using the following method:

[0026] Using real-time temperature and real-time humidity as two target parameters, the mean value of the target parameters of the building structure over the past hour and the standard deviation of the target parameters over the past hour are obtained sequentially.

[0027] Data analysis is performed on the target parameters of the building structure at time t. If the target parameters satisfy... Then the target parameter is marked as an outlier, and the outlier parameters are removed; where Z(j) t The value of the target parameter at time t; Let be the mean of the target parameter; σ(j) be the standard deviation of the target parameter; j be 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 temperature signal loss caused by removing abnormal parameters, a linear interpolation algorithm is used to complete the target parameters;

[0029] Through formula The environmental adaptation parameters were calculated.

[0030] In the formula, I E (t) represents the environmental adaptation parameters 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 The optimal curing temperature for building materials; H opt The optimal humidity for the curing of building materials.

[0031] Preferably, for S2, the method for calculating the dynamic weights of the multidimensional feature indicators using a dynamic weight model is as follows:

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

[0033] Through formula The entropy value of the characteristic index is calculated based on the sliding window period at time t;

[0034] In the formula, Pik h (t) represents the proportion of the k-th sample value of the feature index within the sliding window;

[0035] Through formula The dynamic weight W of the feature index at time t is calculated.h (t);

[0036] wherein d is the feature index number, h = [1, n], and n is the total number of feature indexes.

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

[0038] wherein Pik(t) is the proportion of the kth sample value of the feature index in the sliding window. h

[0039] The dynamic weight Wk(t) of the feature index at time t is calculated by the formula h

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

[0041] Preferably, for S3, the normalization processing of the feature index includes the following methods:

[0042] S31, the structural stability parameter is normalized by the following method:

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

[0044] wherein D is the maximum displacement threshold allowed by the design. max

[0045] S32, the material strength parameter is normalized by the following method:

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

[0047] wherein a and b are material test calibration coefficients.

[0048] The material strength score I2(t) is calculated by the minimum value comparison function design wherein f is the design strength requirement.

[0049] For example, if the material strength score I2(t) < 0.9, the maintenance scheme is automatically adjusted, such as starting to spray or heating.

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

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

[0052] The mass evaluation parameter of the building structure at time t is calculated;

[0053] In the formula, I h (t) is the score corresponding to the characteristic index; delta m (t) is the abnormal fluctuation function of the mth characteristic index; lambda is the penalty intensity coefficient;

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

[0055] In the formula, mu h (t) is the mean value of the characteristic index in the sliding window period, gamma h (t) is the standard deviation of the characteristic index in the sliding window period.

[0056] Preferably, for S4, the mass evaluation parameter is subjected to data analysis to determine the quality management detection method as follows:

[0057] The mass evaluation parameter Q total is monitored in real time, and the mass evaluation parameter Q total is compared with the preset mass evaluation first threshold Q Fth and the mass evaluation second threshold Q Sth in turn;

[0058] If Q total > Q Fth , it is determined that the building structure is normal, and the building structure is continuously monitored;

[0059] If Q Sth ≤ Q total < Q Fth , it is determined that the building structure is slightly abnormal, a pre-warning is given, and a technical personnel is informed to manually review;

[0060] If Q total < Q Sth , it is determined that the building structure is seriously abnormal, an alarm is given, and an emergency shutdown is performed.

[0061] The application further discloses a building supervision quality management detection system applying the building supervision quality management detection method, and specifically comprises:

[0062] A data acquisition and processing module is used for deploying a sensor network and a storage terminal, acquiring real-time data of the construction progress of the building project, and updating the data to the storage terminal in real time, acquiring the project parameters synchronized by the sensor network and the Internet of Things terminal in real time, processing the project parameters, determining the structure stability parameter, the material strength parameter, the process compliance parameter and the environment adaptation parameter of the building structure, and taking the parameters as the multi-dimensional characteristic indexes for determining the quality management detection method.

[0063] A dynamic weight calculation module is configured to calculate the dynamic weight of the multi-dimensional characteristic index through a dynamic weight model, so as to avoid subjectivity of fixed weight and adapt to requirements of different construction stages.

[0064] A data analysis module is configured to normalize the characteristic index and obtain the normalized score of the multi-dimensional characteristic index.

[0065] A quality management detection module is configured to fuse the multi-dimensional characteristic parameters by using a comprehensive quality evaluation model based on the normalized score and the dynamic weight of the multi-dimensional characteristic index, obtain the quality evaluation parameter considering the normalized score and the dynamic weight of the multi-dimensional characteristic index, and perform data analysis on the quality evaluation parameter to determine the quality management detection method.

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

[0067] (1) The structural stability parameter, the material strength parameter, the process compliance parameter and the environmental adaptation parameter of the building structure are determined by performing data processing on the engineering parameters, and are used as the multi-dimensional characteristic index for determining the quality management detection method. The dynamic weight of the multi-dimensional characteristic index is calculated through a dynamic weight model, so as to avoid subjectivity of fixed weight and adapt to requirements of different construction stages.

[0068] (2) The normalized score of the multi-dimensional characteristic index is obtained by normalizing the characteristic index. The quality evaluation parameter considering the normalized score and the dynamic weight of the multi-dimensional characteristic index is obtained by fusing the multi-dimensional characteristic parameters by using a comprehensive quality evaluation model. The building structure is fully covered in structure, material, process and environment, and the management blind area is reduced. Through this method, the quality state can be mastered in real time by the engineering manager, the traditional engineering processing method based on post-repair is changed into a processing method based on pre-prevention, and the rework cost is significantly reduced. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 A method flowchart of the embodiment one of the present application is shown. DETAILED DESCRIPTION

[0070] Embodiment one, as shown in the figure, the present application provides a building supervision quality management detection method, comprising the following steps: Figure 1

[0071] ​S1, deploy a sensor network and a storage terminal, collect real-time data on the construction progress of the construction project, and update the data 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, determine the structural stability parameters, material strength parameters, process compliance parameters, and environmental adaptation parameters of the building structure, and use the parameters as multi-dimensional feature indexes for determining the quality management detection method;

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

[0073] S11, calculate the structural stability parameters of the building structure by 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] For example, when the structural stability parameter ΔD(t) > 0.8D max , a secondary warning is triggered; when D max is exceeded, the construction is automatically suspended and the engineer is notified;

[0078] S12, calculate the material strength parameters of the building structure by 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-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 engineering scheme, determine the preset standard construction requirements and standard construction process in the construction engineering scheme, and count the number of the standard construction process to obtain the total number of the standard construction process;

[0083] Obtain the construction image of the construction site based on computer vision technology, identify the construction image through the YOLO algorithm, determine the real-time construction process at the moment, and judge whether the worker's action and the use of tools meet the standard operation requirements, if yes, mark the real-time construction process as the correct process; count the total number of correct processes;

[0084] The stability parameter is calculated by the formula

[0085] In the formula, 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 is the total number of standard construction processes;

[0086] For example, if the key steps such as setting up the circuit are not executed in sequence, the system locks the subsequent procedures and pushes the rectification instructions;

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

[0088] The real-time temperature and the real-time humidity are taken as two target parameters respectively, and the mean value of the target parameters of the building structure in the past 1 hour and the standard deviation of the target parameters in the past 1 hour are obtained in sequence;

[0089] The data of the target parameters of the building structure at time t are analyzed, if the target parameters meet , the target parameters are marked as abnormal parameters, and the abnormal parameters are removed; 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); wherein, 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 by a temperature sensor, and the real-time humidity is collected by a humidity sensor;

[0090] For the loss of temperature signal caused by removing the abnormal parameters, a linear difference algorithm is used to complete the target parameters;

[0091] The environmental adaptation parameter is calculated by the formula

[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 the real-time humidity respectively; T​​opt Optimal curing temperature for building materials; H opt Optimal curing humidity for building materials;

[0093] Exemplary, when I E When (t) < 0.7, automatically start curing equipment such as a spray system, a heating blanket, to adjust the environment;

[0094] S2, calculate the dynamic weight of the multi-dimensional feature index through a dynamic weight model, avoid the subjectivity of fixed weight, and adapt to the needs of different construction stages;

[0095] For S2, the method for calculating the dynamic weight of the multi-dimensional feature index through a dynamic weight model is:

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

[0097] The entropy value of the feature index based on the sliding window period at time t is calculated by the formula

[0098] In the formula, Pik h (t) is the proportion of the kth sample value of the feature index in the sliding window;

[0099] The dynamic weight W h (t) of the feature index at time t is calculated by the formula

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

[0101] S3, normalize the feature index to obtain the normalized score of the multi-dimensional feature index;

[0102] For S3, the normalization of the feature index includes the following methods:

[0103] S31, normalize the structural stability parameter, the method is as follows:

[0104] The structural stability score I1(t) is calculated by the formula

[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 the structural design code such as the Chinese "Building Structure Load Code" GB 50009;

[0106] S32, normalize the material strength parameter, 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, a and b are material test calibration coefficients, and are obtained through pressure test data of standard test blocks in the same period of curing;

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

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

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

[0112] S4, based on the normalized score and the dynamic weight of the multi-dimensional feature index, the multi-dimensional feature parameters are fused by using a comprehensive quality evaluation model to obtain the quality evaluation parameter of the normalized score and the dynamic weight of the multi-dimensional feature index, and the quality evaluation parameter is analyzed to determine the quality management detection method;

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

[0114]

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

[0116] Wherein, I h (t) is the score corresponding to the feature index; δ m (t) is the abnormal fluctuation function of the mth feature index; λ is the penalty intensity coefficient; it is to 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 feature index in the sliding window period, and γ h (t) is the standard deviation of the feature index in the sliding window period;

[0119] It is to be noted that the formula part in the formula is is a penalty term, which triggers a penalty mechanism and reduces the total score when the score of a certain parameter deviates from the historical mean value by more than 1 times the standard deviation.​

[0120] For S4, the quality evaluation parameter is subjected to data analysis, and the quality management detection method is determined as follows:

[0121] The quality evaluation parameter Q total is subjected to real-time monitoring, and the quality evaluation parameter Q total is compared with a preset quality evaluation first threshold Q Fth and a quality evaluation second threshold Q Sth in sequence.

[0122] If Q total > Q Fth , it is determined that the building structure is normal, and the building structure continues to be monitored.

[0123] If Q Sth ≤ Q total < Q Fth , it is determined that the building structure is slightly abnormal, a pre-warning is given, and a technical personnel is informed to manually review.

[0124] If Q total < Q Sth , it is determined that the building structure is seriously abnormal, an alarm is given, and an emergency shutdown is performed.

[0125] In embodiment two, the building supervision quality management detection system proposed by the application is applied to the building supervision quality management detection method proposed in embodiment one, and specifically includes:

[0126] A data acquisition and processing module is configured to deploy a sensor network and a storage terminal, acquire real-time data of the construction progress of a building project, and update the data to the storage terminal in real time, acquire project parameters synchronized by the sensor network and the Internet of Things terminal in real time, process the project parameters, determine structure stability parameters, material strength parameters, process compliance parameters, and environmental adaptation parameters of the building structure, and use the parameters as multi-dimensional feature indexes for determining the quality management detection method.

[0127] A dynamic weight calculation module is configured to calculate dynamic weights of the multi-dimensional feature indexes by using a dynamic weight model, avoid subjectivity of fixed weights, and adapt to different construction stage requirements.

[0128] A data analysis module is configured to normalize the feature indexes and acquire normalized scores of the multi-dimensional feature indexes.

[0129] A quality management detection module is configured to fuse the multi-dimensional feature parameters by using a comprehensive quality evaluation model based on the normalized scores and the dynamic weights of the multi-dimensional feature indexes, acquire quality evaluation parameters considering the normalized scores and the dynamic weights of the multi-dimensional feature indexes, and determine the quality management detection method by analyzing the quality evaluation parameters.

[0130] The embodiments of the present application are described in detail above with reference to the accompanying drawings, but the present application is not limited to the embodiments. Various changes that one skilled in the art would make within the scope of the knowledge of the art without departing from the spirit of the present application are possible.

Claims

1. A method for quality management and inspection in construction supervision, characterized in that, Includes the following steps: S1. Deploy sensor networks and storage terminals to collect real-time data on the construction progress of building projects and update the data to the storage terminals in real time. Obtain engineering parameters synchronized in real time by sensor networks and IoT terminals, 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 quality management testing methods. Methods for processing engineering parameters include: The environmental adaptability parameters of a building structure are determined using the following method: Using real-time temperature and real-time humidity as two target parameters, the mean value of the target parameters of the building structure over the past hour and the standard deviation of the target parameters over the past hour are obtained sequentially. Data analysis is performed on the target parameters of the building structure at time t. If the target parameters satisfy... If the target parameter is an outlier, then the outlier parameter is removed; where, The value of the target parameter at time t; The mean of the target parameter; Let be the standard deviation of the target parameter; j is the target parameter number, j∈(1,2); where j=1 represents real-time temperature; j=2 represents real-time humidity; For the temperature signal loss caused by removing abnormal parameters, a linear interpolation algorithm is used to complete the target parameters; Through formula The environmental adaptation parameters were calculated. In the formula, Let α1 and α2 be the environmental adaptation parameters of the building structure at time t; α1 and α2 are the attenuation coefficients of real-time temperature and real-time humidity, respectively. The optimal curing temperature for building materials; The optimal curing humidity for building materials; S2, calculate the dynamic weights of multi-dimensional feature indicators through a dynamic weight model to avoid the subjectivity of fixed weights and adapt to the needs of different construction stages; The method for calculating the dynamic weights of multidimensional feature indicators using a dynamic weight model is as follows: Establish a sliding window period for the feature indicators, count the amount of data in the sliding window period and label it as Nh; h is the feature indicator number, h=[1,n], and n is the total number of feature indicators; Through formula The entropy value of the characteristic index is calculated based on the sliding window period at time t; In the formula, This represents the percentage of the k-th sample value of the feature index within the sliding window. Through formula The dynamic weights of the feature index at time t are calculated. ; In the formula, d is the feature index number, h=[1,n], and n is the total number of feature indicators; S3. Normalize the feature indicators to obtain normalized scores for multidimensional feature indicators. S4. Based on the normalized scores and dynamic weights of multidimensional feature indicators, the multidimensional feature parameters are fused using a comprehensive quality assessment model to obtain quality assessment parameters that take into account both the normalized scores and dynamic weights of multidimensional feature indicators. Data analysis is then performed on the quality assessment parameters to determine the quality management testing method. The expression for the comprehensive quality assessment model is: ； The quality assessment parameters of the building structure at time t are calculated. In the formula, The score corresponding to the feature index; Let m be the abnormal fluctuation function of the m-th characteristic indicator; This is the penalty intensity coefficient; Score the structural stability; To score the material strength; Scoring for process compliance; Assess environmental adaptability. The expression for the abnormal fluctuation function is: ； In the formula, This represents the mean of the characteristic index over the sliding window period. is the standard deviation of the characteristic index over the sliding window period.

2. The method for quality management and testing of building supervision according to claim 1, characterized in that, For S1, methods for processing engineering parameters also include: The structural stability parameters of a building structure are calculated using a stability assessment formula, which is as follows: ； In the formula, The structural stability parameters of the building structure at time t; Let t be the real-time displacement values ​​of the building materials in the x, y, and z directions; The initial reference value is determined before the construction of the building structure; i is the number of the three directions x, y, and z.

3. The method for quality management and testing of building supervision according to claim 2, characterized in that, For S1, methods for processing engineering parameters also include: The material strength parameters of a building structure are calculated using a material strength assessment formula, which is as follows: ； In the formula, These are the material strength parameters of the building structure at time t; The temperature of the building structure at time t is the real-time temperature. The reference temperature for the building structure; The sampling interval is denoted as .

4. The construction supervision quality management and testing method according to claim 3, characterized in that, For S1, methods for processing engineering parameters also include: The following methods are used to determine the process compliance parameters for building structures: Acquire and identify building engineering plans, determine the pre-set standard construction requirements and standard construction processes in the building engineering plans, and count the number of standard construction processes to obtain the total number of standard construction processes. Construction images of the construction site are acquired using computer vision technology. The YOLO algorithm is used to identify the construction images, determine the current real-time construction process, and judge whether the workers' actions and tools used meet the standard operating requirements. If they do, the real-time construction process is marked as the correct process; the total number of correct processes is counted. Through formula The stability parameters were calculated. In the formula, These are the process compliance parameters for the building structure at time t. Total number of correct processes; To standardize the total number of construction processes.

5. The construction supervision quality management and testing method according to claim 3, characterized in that, For S3, the normalization of feature indicators includes the following methods: S31, The structural stability parameters are normalized as follows: Through formula The structural stability score was calculated. ; In the formula, To design the maximum allowable displacement threshold; S32, the material strength parameters are normalized using the following method: Through formula The real-time strength of the building structure is calculated; In the formula, a and b are both material test calibration coefficients; Comparison function by minimum value The material strength score was calculated. ; In the formula, To meet design strength requirements; S33 uses process compliance parameters and environmental adaptability parameters as separate criteria for process compliance scoring. and environmental adaptability score .

6. The method for quality management and testing of building supervision according to claim 5, characterized in that, For S4, data analysis was conducted on the quality assessment parameters to determine the following quality management testing methods: Quality assessment parameters Real-time monitoring will be conducted, and quality assessment parameters will be used. Compared with the preset first threshold for quality assessment and the second threshold for quality assessment Compare them in sequence; like > If the building structure is found to be normal, monitoring of the building structure will continue. like ≤ < If the building structure is found to be slightly abnormal, an early warning will be issued, and technical personnel will be notified to conduct a manual review. like < If the building structure is found to be seriously abnormal, an alarm will be issued and work will be stopped immediately.

7. A construction supervision quality management and testing system, employing the construction supervision quality management and testing method according to any one of claims 1 to 6, characterized in that, Specifically, it includes: The data acquisition and processing module is used to deploy sensor networks and storage terminals to collect real-time data on the construction progress of building projects and update it to the storage terminal in real time. It obtains engineering parameters synchronized in real time by sensor networks and IoT terminals, processes the engineering parameters, and determines the structural stability parameters, material strength parameters, process compliance parameters, and environmental adaptability parameters of building structures. These parameters are used as multi-dimensional characteristic indicators to determine quality management testing methods. The dynamic weight calculation module is 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 normalized scores for multi-dimensional feature indicators. The quality management and testing module is used to fuse multidimensional feature parameters based on normalized scores and dynamic weights of multidimensional feature indicators using a comprehensive quality assessment model. This results in quality assessment parameters that take into account both normalized scores and dynamic weights of multidimensional feature indicators. The module also performs data analysis on the quality assessment parameters to determine the quality management and testing methods.

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