Building quality safety management method based on big data

Abstract

The invention discloses a building quality safety management method based on big data, and relates to the technical field of building management.The risk of a glass curtain wall in the transportation process is monitored and evaluated in real time through integration of various sensors and data analysis means, and the risk of the glass curtain wall in the transportation process is monitored and evaluated through collection and analysis of pressure, displacement and vibration data. According to the method, risks such as unstable installation, uneven pressure and excessive vibration possibly occurring in the transportation process can be recognized in time, a multi-level risk level detection and division mechanism is provided for comprehensive assessment of various risk data, detection measures of different levels are adopted according to the transportation risk assessment value of the glass curtain wall, and the risk assessment accuracy of the glass curtain wall is improved. On the whole, accurate management of the glass curtain wall transportation process can be achieved, the building quality safety guarantee level is improved, risks and losses are reduced, and the safety and efficiency of construction and transportation links are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of building management, and in particular to a building quality and safety management method based on big data. Background Art

[0002] A Chinese invention with application publication number CN118657446AD discloses a construction project quality and safety tracking management system based on big data, including: a construction project information acquisition module, a warehouse material detection module, a warehouse material analysis module, a construction material detection module, a completed building detection module, a material quality and safety analysis module, a quality and safety tracking management terminal and a database; by comprehensively analyzing the quality and safety of the engineering materials corresponding to the construction project from three aspects: the accumulated materials, construction materials and completed materials of the construction project, the engineering quality of the construction project is guaranteed to the greatest extent, and material defects and problems in the construction are reduced, thereby extending the service life of the building, reducing the cost of maintenance and restoration, and improving the sustainable development capacity.

[0003] In construction projects, glass curtain walls as building materials are important building facade materials and are widely used in modern high-rise buildings. Due to the heavy weight, large volume and fragility of glass curtain walls, the safety of glass curtain walls during transportation and installation has always been an important issue in the industry. With the continuous development of big data, the Internet of Things and sensor technology, more and more construction industries have begun to explore how to use advanced technologies to improve the safety and efficiency of transportation and installation. However, most of the existing technologies focus on monitoring a single link, lacking effective management, detection and evaluation of the glass curtain wall transportation process, and also lacking comprehensive monitoring and evaluation of safety risks during the transportation of glass curtain walls. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a construction quality and safety management method based on big data, which solves the problems in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A construction quality and safety management method based on big data, comprising: Step 1: Place the glass curtain wall to be transported on the mounting rack, mark the installation position of the mounting rack, and number the glass curtain wall to be transported, and place each numbered glass curtain wall at the installation position of the corresponding mounting rack and store and record it; Step 2: During transportation, pressure data of the glass curtain wall placed on the mounting rack is collected, and the fixing risk of the glass curtain wall during transportation is evaluated by analyzing the pressure data to determine the fixing risk impact value; Step 3: Collect displacement data of the glass curtain wall placed on the mounting frame, evaluate the displacement risk of the glass curtain wall during transportation by analyzing the displacement data, and determine the impact value of the displacement risk; Step 4: Collect vibration data of the mounting frame on which the glass curtain wall is placed, evaluate the vibration risk of the glass curtain wall during transportation by analyzing the vibration data, and determine the vibration risk impact value; Step 5: Perform transportation risk assessment based on the fixed risk impact value and displacement risk impact value of each glass curtain wall placed on the mounting frame, as well as the vibration risk value of the glass curtain wall during transportation evaluated by analyzing the vibration data, determine the transportation risk assessment value of each glass curtain wall, and divide the inspection level of each glass curtain wall according to the transportation risk assessment value.

[0006] As a further solution of the present invention: In the step 1, the specific method of placing each numbered glass curtain wall at the installation position of the corresponding installation frame and storing and recording it is as follows: AS1: Mark the installation position of the mounting bracket ,in, , The total number of installation positions for the installation racks in this transport; AS2: Number and identify the glass curtain walls to be transported. ,in, , is the total number of glass curtain walls that need to be transported this time, and ; AS3: Then identify each number The glass curtain wall is placed in the installation position of the corresponding mounting frame Storage records are stored on.

[0007] As a further solution of the present invention: during the transportation process, pressure data of the glass curtain wall placed on the mounting frame is collected, and the fixing risk of the glass curtain wall during the transportation process is evaluated by analyzing the pressure data. The specific method for determining the fixing risk impact value is as follows: BS1: For the glass curtain walls placed on the mounting frame, all the glass curtain walls are taken as the target glass curtain walls and the fixed risk impact value is determined through the following steps; BS2: Get the current pressure value of each sensor of the target glass curtain wall and mark it as ,and ,in Indicates Sensors, Indicates the current moment, Represents the total number of sensors on the target glass curtain wall, thereby determining the target glass curtain wall at the current moment The pressure distribution of : ; BS3: Then, the pressure values ​​of different sensors are unified into the same dimension, and each pressure value is normalized to obtain the normalized pressure distribution data. : ; BS4: Set in time window Determine the pressure fluctuation in the contact area between the glass curtain wall and the mounting frame ; BS5: Then calculate the rate of change of pressure data using the following formula : ; in, is the collection time interval, and ; The change rates of all sensors on the target curtain wall are counted, and the total number of pressure mutations is obtained by the following method: : ; in, is an indicator function, which takes the value of 1 when the pressure change rate exceeds the threshold, otherwise it takes the value of 0. is the preset threshold; BS6: According to pressure fluctuation and the total number of pressure mutations , determine the current time Fixed risk impact value : BS7: Obtain the total time of the target glass curtain wall during transportation, recorded as , by using the formula ,Sure At each pressure data collection moment, the fixed risk impact value at each collection moment is obtained through steps BS2-BS6 ,in, ; Determine the fixed risk impact value of the target glass curtain wall during transportation by the following formula: : ; in, The risk influencing factor.

[0008] As a further solution of the present invention: in the step 3, the displacement data of the glass curtain wall placed on the mounting frame is collected, and the displacement risk of the glass curtain wall during transportation is evaluated by analyzing the pressure data. The specific method for determining the displacement risk impact value is: CS1: The glass curtain walls placed on the mounting frame are taken as the target glass curtain walls and the displacement risk impact value is determined through the following steps; CS2: By setting the acquisition time interval The relative displacement between the target glass curtain wall and the mounting frame is collected and marked as ; Get the total time of the target glass curtain wall during transportation, recorded as , by using the formula ,Sure displacement data collection time, among which, ; Then, through the formula Determine the total displacement of the target glass curtain wall during transportation ; CS3: Get The relative displacement between the target glass curtain wall and the mounting frame is the largest at the moment of displacement data collection , recorded as ; Combined with the total displacement of the target glass curtain wall during transportation , the displacement risk impact value of the target glass curtain wall during transportation is determined by the following formula: : ; in, and is the weight coefficient.

[0009] As a further solution of the present invention: in the step 4, the vibration data of the mounting frame on which the glass curtain wall is placed is collected, and the vibration risk of the glass curtain wall during transportation is evaluated by analyzing the vibration data. The specific method for determining the vibration risk impact value is as follows: DS1: Set the collection time interval to , collect the vibration data x(t) at each time point during the transportation process, and perform denoising on the original vibration data x(t); then use the sliding average or exponential smoothing method to smooth the data; DS2: Then determine a time window W and calculate the variance of the vibration signal within the time window W. The time window W means that z vibration data are collected within the time window W. The variance of the vibration signal within the time window W is expressed by the following formula: ; in, It is expressed as the variance of the vibration signal within the time window W, It is represented as the bth collected vibration data among the zth vibration data, and , is the mean value of the vibration data in the time window W: ; DS3: Then calculate the vibration change rate of the vibration data within the time window W using the following formula: : ; Get the total number z of vibration data collected in the time window W and get the preset limit threshold , all the change rates in the time window W are counted by the following formula Exceeding the preset limit threshold Quantity: ; in, is an indicator function, which takes the value of 1 when the vibration change rate exceeds the threshold, otherwise it takes the value of 0. DS4: Get the variance of the vibration signal within the time window W and all the rates of change in the time window W Exceeding the preset limit threshold Number of , the vibration risk impact value within the time window W is determined by the following formula : ; DS5: Get the total time taken by the glass curtain wall during transportation, recorded as ; Then use the formula , determine the total time window number of the glass curtain wall during transportation , through steps DS1-DS4, determine The vibration risk impact value corresponding to the time window is marked as ,in, ; At the same time, the vibration risk impact value is determined by the following formula ; ; in, is the weight coefficient.

[0010] As a further solution of the present invention: in the step 5, the transportation risk assessment value is determined according to the fixed risk impact value and the displacement risk impact value of each glass curtain wall placed on the mounting frame, and the vibration risk value of the glass curtain wall during transportation is assessed by analyzing the vibration data. The specific method for determining the transportation risk assessment value of each glass curtain wall is as follows: Each glass curtain wall placed on the mounting rack is taken as the target glass curtain wall to determine its transportation risk assessment value, obtain the fixed risk impact value and displacement risk impact value of the target glass curtain wall, and analyze the vibration data to evaluate the vibration risk value of the glass curtain wall during transportation to obtain the transportation risk assessment value. Specifically, the transportation risk assessment value of the target glass curtain wall is determined by the following formula: : ; in, , and is the weight coefficient, is the fixed risk impact value, is the displacement risk impact value, is the vibration risk impact value.

[0011] As a further solution of the present invention: the specific method of classifying the detection level of each glass curtain wall according to the transportation risk assessment value is: According to the transportation risk assessment value of the target glass curtain wall Classification of detection levels: like , indicating that the transportation risk of the target glass curtain wall is relatively low, and the first-level inspection is adopted; like , indicating that the transportation risk of the target glass curtain wall is relatively high, and secondary inspection is adopted; like , indicating that the transportation risk of the target glass curtain wall is relatively high, and the third-level inspection is adopted; in, , is the default value, and .

[0012] The present invention provides a construction quality and safety management method based on big data. Compared with the prior art, it has the following beneficial effects: The building quality and safety management method based on big data proposed in the present invention monitors and evaluates the risks of glass curtain walls in the transportation process in real time by integrating multiple sensors and data analysis methods. Through the collection and analysis of pressure, displacement and vibration data, this method can timely identify the risks of unstable installation, uneven pressure, excessive vibration, etc. that may occur during transportation. The comprehensive evaluation of various risk data provides a multi-level risk level detection and classification mechanism. According to the transportation risk assessment value of the glass curtain wall, different levels of detection measures are taken to ensure that high-risk glass curtain walls are more strictly detected and managed. Overall, the present invention can achieve accurate management of the glass curtain wall transportation process, improve the level of building quality and safety assurance, reduce risks and losses, and optimize the safety and efficiency of construction and transportation links. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described below in conjunction with the accompanying drawings.

[0014] Figure 1 It is a flowchart of the steps of the construction quality and safety management method based on big data of the present invention; DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] For example, see Figure 1 ,The present invention provides a construction quality and safety management method based on big data, including; Step 1: Place the glass curtain wall to be transported on the mounting rack, mark the installation position of the mounting rack, and number the glass curtain wall to be transported, and place each numbered glass curtain wall at the installation position of the corresponding mounting rack and store and record it; The specific method of placing the glass curtain wall to be transported on the mounting frame, marking the mounting position of the mounting frame, and numbering the glass curtain wall to be transported, and placing each numbered glass curtain wall at the mounting position of the corresponding mounting frame and storing and recording the same is as follows: AS1: Mark the installation position of the mounting bracket ,in, , The total number of installation positions for the installation racks in this transport; AS2: Number and identify the glass curtain walls to be transported. ,in, , is the total number of glass curtain walls that need to be transported this time, and ; AS3: Then identify each number The glass curtain wall is placed in the installation position of the corresponding mounting frame Storage records are stored on It should be noted that when transporting glass curtain walls, mounting racks or special fixing devices are required to ensure safety and avoid glass breakage or displacement. Glass curtain walls are usually large-area glass panels that are easily damaged during transportation. Placing the glass curtain walls to be transported on mounting racks mentioned above specifically refers to effectively fixing the glass curtain walls by using appropriate suction cup devices, cushions, support racks, etc.; Before transporting the glass curtain wall, it is safely placed on the mounting frame, and the position of each mounting frame is marked. At the same time, each piece of glass curtain wall is numbered and the specific position of its mounting frame is recorded. This measure effectively ensures the positioning accuracy and traceability of the glass curtain wall during transportation. Since glass curtain walls are usually large and fragile, they may be displaced or lost during transportation. Marking and numbering can greatly reduce these risks, thereby avoiding glass breakage or confusion. In addition, by recording the position of the mounting frame, it is convenient to track and manage each piece of glass curtain wall in real time during transportation, which improves management efficiency and accuracy and ensures safety and standardization during transportation. Step 2: During transportation, pressure data of the glass curtain wall placed on the mounting rack is collected, and the fixing risk of the glass curtain wall during transportation is evaluated by analyzing the pressure data to determine the fixing risk impact value; Specifically, the pressure data of all glass curtain walls placed on the mounting frame are collected mainly by installing pressure sensors at key locations where the glass curtain walls and the mounting frame contact, and recording the pressure value of each sensor in real time; During the transportation process, pressure data of the glass curtain wall placed on the mounting frame is collected, and the fixing risk of the glass curtain wall during transportation is evaluated by analyzing the pressure data. The specific method for determining the fixing risk impact value is as follows: BS1: For the glass curtain walls placed on the mounting frame, all the glass curtain walls are taken as the target glass curtain walls and the fixed risk impact value is determined through the following steps; BS2: Get the current pressure value of each sensor of the target glass curtain wall and mark it as ,and ,in Indicates Sensors, Indicates the current moment, Represents the total number of sensors on the target glass curtain wall, thereby determining the target glass curtain wall at the current moment The pressure distribution of : ; BS3: Then unify the pressure values ​​of different sensors into the same dimension using the following formula: , normalize each pressure value to obtain normalized pressure distribution data : ; in, and The current moment Minimum and maximum pressure values ​​for all sensors; BS4: Set in time window The pressure fluctuation degree in the contact area between the glass curtain wall and the mounting frame is determined by the following formula: : ; in, For the current moment The pressure mean; pressure fluctuation , indicating that the more uneven the pressure distribution, the higher the possible risk during transportation; BS5: Then calculate the rate of change of pressure data using the following formula : ; in, is the collection time interval, and ; The change rates of all sensors on the target curtain wall are counted to obtain the total number of pressure mutations. : ; in, is an indicator function, which takes the value of 1 when the pressure change rate exceeds the threshold, otherwise it takes the value of 0. It is a preset threshold value, which is determined by professional staff; BS6: According to pressure fluctuation and the total number of pressure mutations , the current time is determined by the following formula Fixed risk impact value : ; in, , is the weight coefficient, which is determined by professional staff; BS7: Obtain the total time of the target glass curtain wall during transportation, recorded as , by using the formula ,Sure At each pressure data collection moment, the fixed risk impact value at each collection moment is obtained through steps BS2-BS6 ,in, ; Determine the fixed risk impact value of the target glass curtain wall during transportation by the following formula: : ; in, It is a risk influencing factor, which is set by professional staff; By collecting real-time pressure data of the contact area between the glass curtain wall and the mounting frame during transportation, the pressure distribution of the glass curtain wall during transportation can be analyzed in detail; the deployment of pressure sensors can monitor pressure fluctuations in key areas, and combined with the fluctuation and mutation of pressure data, it can effectively evaluate the installation risks that may exist during transportation; for example, if the pressure in a certain area is too high or too low, it may mean that the support of the fixture is uneven or there is a risk of loosening, which may cause the glass curtain wall to shift or be damaged; by analyzing pressure data, potential risks can be identified in time and preventive measures can be taken, thereby improving transportation safety and reducing transportation accidents caused by uneven pressure or improper fixation; Step 3: Collect displacement data of the glass curtain wall placed on the mounting frame, evaluate the displacement risk of the glass curtain wall during transportation by analyzing the displacement data, and determine the impact value of the displacement risk; Specifically, the displacement data of all glass curtain walls placed on the mounting frame are collected mainly by installing displacement sensors and tilt sensors. The displacement sensors record the relative displacement between the glass curtain wall and the mounting frame in real time, and the tilt sensors are used to record the tilt angle of the glass curtain wall in real time. The displacement data of the glass curtain wall placed on the mounting frame is collected, and the displacement risk of the glass curtain wall during transportation is evaluated by analyzing the pressure data. The specific method for determining the displacement risk impact value is as follows: CS1: The glass curtain walls placed on the mounting frame are taken as the target glass curtain walls and the displacement risk impact value is determined through the following steps; CS2: By setting the acquisition time interval The relative displacement between the target glass curtain wall and the mounting frame is collected and marked as ; Get the total time of the target glass curtain wall during transportation, recorded as , by using the formula ,Sure displacement data collection time, among which, , in this embodiment, , ; Then, through the formula Determine the total displacement of the target glass curtain wall during transportation ; CS3: Get The relative displacement between the target glass curtain wall and the mounting frame is the largest at the moment of displacement data collection , recorded as ; Combined with the total displacement of the target glass curtain wall during transportation , the displacement risk impact value of the target glass curtain wall during transportation is determined by the following formula: : ; in, and is the weight coefficient, which is determined by professional staff; The collection of displacement data can provide information about the displacement of the glass curtain wall during transportation. This step helps to discover potential installation instability problems of the glass curtain wall in advance, avoid the risk of excessive displacement of the glass curtain wall during transportation, improve the stability of the installation process, and ensure the safe transportation of the glass curtain wall. Step 4: Collect vibration data of the mounting frame on which the glass curtain wall is placed, evaluate the vibration risk of the glass curtain wall during transportation by analyzing the vibration data, and determine the vibration risk impact value; Specifically, the vibration data of all mounting frames where the glass curtain walls are placed on the mounting frames are collected mainly by installing vibration sensors; The specific method of collecting vibration data of the mounting frame on which the glass curtain wall is placed and evaluating the vibration risk of the glass curtain wall during transportation by analyzing the vibration data and determining the vibration risk impact value is as follows: DS1: Set the collection time interval to , collect vibration data x(t) at each time point during the transportation process, denoise the original vibration data x(t) to remove the high-frequency components caused by noise and ensure the reliability of the data; then use the sliding average or exponential smoothing method to smooth the data to reduce short-term fluctuations and make the main trends clearer; DS2: Determine a time window W and calculate the variance of the vibration signal within the time window W. The time window W indicates that z vibration data are collected within the time window W. The variance of the vibration signal within the time window W is expressed by the following formula: ; in, It is expressed as the variance of the vibration signal within the time window W, It is represented as the bth collected vibration data among the zth vibration data, and , is the mean value of the vibration data in the time window W: ; DS3: Then calculate the vibration change rate of the vibration data within the time window W using the following formula: : ; Get the total number z of vibration data collected in the time window W and get the preset limit threshold , all the change rates in the time window W are counted by the following formula Exceeding the preset limit threshold Quantity: ; in, is an indicator function, which takes the value of 1 when the vibration change rate exceeds the threshold, otherwise it takes the value of 0. DS4: Get the variance of the vibration signal within the time window W and all the rates of change in the time window W Exceeding the preset limit threshold Number of , the vibration risk impact value within the time window W is determined by the following formula : ; DS5: Get the total time taken by the glass curtain wall during transportation, recorded as , in this embodiment, ;; Then use the formula , determine the total time window number of the glass curtain wall during transportation , through steps DS1-DS4, determine The vibration risk impact value corresponding to the time window is marked as ,in, ; At the same time, the vibration risk impact value is determined by the following formula ; ; in, is the weight coefficient, which is determined by professional staff; The collection and analysis of vibration data can effectively identify excessive vibration problems that may occur during transportation. During transportation, the mounting frame may produce certain vibrations due to external reasons or vibrations of the transportation equipment. If these vibrations are too large or too frequent, they may damage the glass curtain wall. By calculating the variance and change rate of the vibration signal, abnormal vibration patterns can be found. Step 5: Perform transportation risk assessment based on the fixed risk impact value and displacement risk impact value of each glass curtain wall placed on the mounting frame, and analyze the vibration data to assess the vibration risk value of the glass curtain wall during transportation, determine the transportation risk assessment value of each glass curtain wall, and classify the inspection level of each glass curtain wall according to the transportation risk assessment value; By comprehensively considering the fixing risk, installation risk and vibration risk of each glass curtain wall, a comprehensive transportation risk assessment can be conducted for each glass curtain wall. This assessment method integrates multiple data and uses quantitative analysis and weight coefficients to conduct risk assessment, so that the risk situation of each glass curtain wall can be accurately quantified and the required inspection level can be determined.

[0017] Embodiment 2: In the specific implementation process, this embodiment is based on Embodiment 1, and the difference from Embodiment 1 is that this embodiment describes the specific content of step 5; The specific method of determining the transportation risk assessment value of each glass curtain wall according to the fixed risk impact value and the displacement risk impact value of each glass curtain wall placed on the mounting frame and the vibration risk value of the glass curtain wall during transportation by analyzing the vibration data, and classifying the inspection level of each glass curtain wall according to the transportation risk assessment value is as follows: P1: Take each glass curtain wall placed on the mounting rack as the target glass curtain wall to determine its transportation risk assessment value, obtain the fixed risk impact value and displacement risk impact value of the target glass curtain wall, and analyze the vibration data to evaluate the vibration risk value of the glass curtain wall during transportation to obtain the transportation risk assessment value. Specifically, the transportation risk assessment value of the target glass curtain wall is determined by the following formula: : ; in, , and is the weight coefficient, which is determined by professional staff. is the fixed risk impact value, is the displacement risk impact value, is the vibration risk impact value; P2: According to the transportation risk assessment value of the target glass curtain wall Classification of detection levels: like , indicating that the transportation risk of the target glass curtain wall is relatively low, and the first-level inspection is adopted; like , indicating that the transportation risk of the target glass curtain wall is relatively high, and secondary inspection is adopted; like , indicating that the transportation risk of the target glass curtain wall is relatively high, and the third-level inspection is adopted; in, , It is a preset value, which is determined by professional staff. ; It should be noted that the higher the level of inspection, the more rigorous the inspection. In this embodiment, the specific inspection methods of the first-level inspection, the second-level inspection and the third-level inspection are determined by professional staff. In this embodiment, basic inspection is carried out on the glass curtain wall of the first-level inspection to ensure that it is not obviously damaged during transportation. The inspection contents include: Surface defect inspection: Check whether there are cracks, chipped corners, scratches or damages on the glass surface that are visible to the naked eye; Edge integrity inspection: Check whether there are cracks, gaps, burrs and other defects that affect safety on the edge of the glass; Preliminary stress detection: Use a polarizing stress meter to detect whether the glass has abnormal stress concentration areas, and preliminarily determine whether there are hidden dangers due to uneven force; Enhanced inspection is carried out on the glass curtain wall of the second-level inspection. The inspection contents include all the contents of the first-level inspection, and the following additional contents are added: Micro-irregularities inside the glass Crack detection: Use ultrasonic or infrared detection technology to determine whether there are invisible cracks or stress damage inside the glass; Stress distribution detection: Use stress sensors to measure stress distribution at different points on the glass to ensure that the glass does not have stress concentration caused by transportation; Glass vibration response analysis: Use vibration testing equipment to analyze whether the glass has microscopic damage or structural fatigue due to transportation vibration; Strict inspection of glass curtain walls for level 3 inspection, including all the contents of level 1 and level 2 inspections, plus the following additional content: Glass structural integrity analysis: Use high-precision industrial CT or X-rays to scan the internal structure of the glass to ensure that there are no potential risks of breakage due to microscopic cracks or inclusions; Three-dimensional stress distribution measurement: Use a high-precision stress tester to measure the overall stress distribution of the glass to ensure that there are no local abnormal stress points.

[0018] Embodiment 3: This embodiment includes all the implementation processes of the above three groups of embodiments in its specific implementation process.

[0019] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0020] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A construction quality and safety management method based on big data, characterized in that: include: Step 1: Place the glass curtain wall to be transported on the mounting rack, mark the installation position of the mounting rack, and number the glass curtain wall to be transported, and place each numbered glass curtain wall at the installation position of the corresponding mounting rack and store and record it; Step 2: During transportation, pressure data of the glass curtain wall placed on the mounting rack is collected, and the fixing risk of the glass curtain wall during transportation is evaluated by analyzing the pressure data to determine the fixing risk impact value; Step 3: Collect displacement data of the glass curtain wall placed on the mounting frame, evaluate the displacement risk of the glass curtain wall during transportation by analyzing the displacement data, and determine the impact value of the displacement risk; Step 4: Collect vibration data of the mounting frame on which the glass curtain wall is placed, evaluate the vibration risk of the glass curtain wall during transportation by analyzing the vibration data, and determine the vibration risk impact value; Step 5: Perform transportation risk assessment based on the fixed risk impact value and displacement risk impact value of each glass curtain wall placed on the mounting frame, as well as the vibration risk value of the glass curtain wall during transportation evaluated by analyzing the vibration data, determine the transportation risk assessment value of each glass curtain wall, and divide the inspection level of each glass curtain wall according to the transportation risk assessment value.

2. The construction quality and safety management method based on big data according to claim 1 is characterized in that: In the step 1, the specific method of placing each numbered glass curtain wall at the installation position of the corresponding installation frame and storing and recording the same is as follows: AS1: Mark the installation position of the mounting bracket ,in, , The total number of installation positions for the installation racks in this transport; AS2: Number and identify the glass curtain walls to be transported. ,in, , is the total number of glass curtain walls that need to be transported this time, and ; AS3: Then identify each number The glass curtain wall is placed in the installation position of the corresponding mounting frame Storage records are stored on.

3. The construction quality and safety management method based on big data according to claim 2 is characterized in that: In the step 2, during the transportation process, pressure data of the glass curtain wall placed on the mounting frame is collected, and the fixing risk of the glass curtain wall during the transportation process is evaluated by analyzing the pressure data. The specific method for determining the fixing risk impact value is as follows: BS1: For the glass curtain walls placed on the mounting frame, all the glass curtain walls are taken as the target glass curtain walls and the fixed risk impact value is determined through the following steps; BS2: Get the current pressure value of each sensor of the target glass curtain wall and mark it as ,and ,in Indicates Sensors, Indicates the current moment, Represents the total number of sensors on the target glass curtain wall, thereby determining the target glass curtain wall at the current moment The pressure distribution is denoted as : ; BS3: Then, the pressure values ​​of different sensors are unified into the same dimension, and each pressure value is normalized to obtain the normalized pressure distribution data. : ; BS4: Set in time window Determine the pressure fluctuation in the contact area between the glass curtain wall and the mounting frame ; BS5: Then calculate the rate of change of pressure data using the following formula : ； in, is the collection time interval, and ; The change rates of all sensors on the target curtain wall are counted, and the total number of pressure mutations is obtained by the following method: : ； in, is an indicator function, which takes the value of 1 when the pressure change rate exceeds the threshold, otherwise it takes the value of 0. is the preset threshold; BS6: According to pressure fluctuation and the total number of pressure mutations , determine the current time Fixed risk impact value : BS7: Obtain the total time of the target glass curtain wall during transportation, recorded as , by using the formula ,Sure At each pressure data collection moment, the fixed risk impact value at each collection moment is obtained through steps BS2-BS6 ,in, ; Determine the fixed risk impact value of the target glass curtain wall during transportation by the following formula: : ； in, The risk influencing factor.

4. The construction quality and safety management method based on big data according to claim 3 is characterized in that: In the step 3, the displacement data of the glass curtain wall placed on the mounting frame is collected, and the displacement risk of the glass curtain wall during transportation is evaluated by analyzing the pressure data. The specific method for determining the displacement risk impact value is as follows: CS1: The glass curtain walls placed on the mounting frame are taken as the target glass curtain walls and the displacement risk impact value is determined through the following steps; CS2: By setting the acquisition time interval The relative displacement between the target glass curtain wall and the mounting frame is collected and marked as ; Get the total time of the target glass curtain wall during transportation, recorded as , by using the formula ,Sure displacement data collection time, among which, ; Then, through the formula Determine the total displacement of the target glass curtain wall during transportation ; CS3: Get The relative displacement between the target glass curtain wall and the mounting frame is the largest at the moment of displacement data collection , recorded as ; Combined with the total displacement of the target glass curtain wall during transportation , the displacement risk impact value of the target glass curtain wall during transportation is determined by the following formula: : ； in, and is the weight coefficient.

5. The construction quality and safety management method based on big data according to claim 1 is characterized in that: In the step 4, the vibration data of the mounting frame on which the glass curtain wall is placed is collected, and the vibration risk of the glass curtain wall during transportation is evaluated by analyzing the vibration data. The specific method for determining the vibration risk impact value is as follows: DS1: Set the collection time interval to , collect the vibration data x(t) at each time point during the transportation process, and perform denoising on the original vibration data x(t); then use the sliding average or exponential smoothing method to smooth the data; DS2: Then determine a time window W and calculate the variance of the vibration signal within the time window W. The time window W means that z vibration data are collected within the time window W. The variance of the vibration signal within the time window W is expressed by the following formula: ； in, Expressed as the variance of the vibration signal within the time window W, It is represented as the bth collected vibration data among the zth vibration data, and , is the mean value of the vibration data in the time window W: ； DS3: Then calculate the vibration change rate of the vibration data within the time window W using the following formula: : ； Get the total number z of vibration data collected in the time window W and get the preset limit threshold , all the change rates in the time window W are counted by the following formula Exceeding the preset limit threshold Quantity: ； in, is an indicator function, which takes the value of 1 when the vibration change rate exceeds the threshold, otherwise it takes the value of 0. DS4: Get the variance of the vibration signal within the time window W and all the rates of change in the time window W Exceeding the preset limit threshold Number of , the vibration risk impact value within the time window W is determined by the following formula : ； DS5: Get the total time taken by the glass curtain wall during transportation, recorded as ; Then use the formula , determine the total time window number of the glass curtain wall during transportation , through steps DS1-DS4, determine The vibration risk impact value corresponding to the time window is marked as ,in, ; At the same time, the vibration risk impact value is determined by the following formula ; ； in, is the weight coefficient.

6. The construction quality and safety management method based on big data according to claim 5 is characterized in that: In step 5, the transportation risk assessment value is determined based on the fixed risk impact value and the displacement risk impact value of each glass curtain wall placed on the mounting frame, and the vibration risk value of the glass curtain wall during transportation is assessed by analyzing the vibration data. The specific method for determining the transportation risk assessment value of each glass curtain wall is as follows: Each glass curtain wall placed on the mounting rack is taken as the target glass curtain wall to determine its transportation risk assessment value, obtain the fixed risk impact value and displacement risk impact value of the target glass curtain wall, and analyze the vibration data to evaluate the vibration risk value of the glass curtain wall during transportation to obtain the transportation risk assessment value. Specifically, the transportation risk assessment value of the target glass curtain wall is determined by the following formula: : ； in, , and is the weight coefficient, is the fixed risk impact value, is the displacement risk impact value, is the vibration risk impact value.

7. The construction quality and safety management method based on big data according to claim 6 is characterized in that: The specific method of classifying the inspection level of each glass curtain wall according to the transportation risk assessment value is as follows: According to the transportation risk assessment value of the target glass curtain wall Classification of detection levels: like , indicating that the transportation risk of the target glass curtain wall is relatively low, and the first-level inspection is adopted; like , indicating that the transportation risk of the target glass curtain wall is relatively high, and secondary inspection is adopted; like , indicating that the transportation risk of the target glass curtain wall is relatively high, and the third-level inspection is adopted; in, , is the default value, and .

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