Bridge high pier construction safety monitoring method and system based on BIM

Abstract

The invention discloses a BIM-based bridge high pier construction safety monitoring method and system, and relates to the technical field of BIM, a dust concentration prediction vector P is calculated through an environment feature set T, high-precision prediction of dust diffusion concentration is realized, and the dust distribution condition of a construction site is more prospective. Then, according to the scheme, the dust fall parameter set D is formulated in combination with the dynamic change of the dust concentration, the dynamic dust fall strategy is executed, the spraying scheme can be adjusted according to the real-time environment, the dust fall efficiency is effectively improved, and the problems of water resource waste and uneven coverage existing in a traditional spraying mode are solved. Besides, through a feedback mechanism of a dust concentration error D, an environment safety index ZS is calculated and compared with a preset construction area trigger threshold ST, iterative optimization of a dust fall parameter set D is achieved, it is ensured that the construction environment is always in a controllable range, and potential safety hazards caused by excessive dust are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of BIM, and in particular to a BIM-based bridge high pier construction safety monitoring method and system. Background Art

[0002] In the field of modern infrastructure construction, bridge engineering is an important part of the transportation network, and its construction quality and safety directly affect the stability and sustainability of the entire transportation system. Especially in the construction projects of highways, large urban interchanges, and cross-river and cross-sea bridges, the construction of high bridge piers has become the focus of engineering safety monitoring due to its high-rise structure, complex stress environment, and difficult construction. BIM technology can integrate multi-dimensional information such as bridge design, construction, and maintenance, and realize visualization, parametric simulation, and risk warning of the construction process, thereby greatly improving construction safety;

[0003] In the invention of Chinese patent application No. 202310860458.3, a BIM-based bridge high pier construction safety monitoring method and system are disclosed, which relates to the field of construction monitoring, wherein the method includes: performing BIM simulation modeling on the first target bridge, outputting the first bridge simulation model; outputting the first geological simulation model; performing model fusion according to the first bridge simulation model and the first geological simulation model, outputting the first construction simulation model; performing construction simulation according to the first construction simulation model, obtaining the first simulation data set; inputting the first simulation data set into the multivariate monitoring model, performing safety monitoring according to the multivariate monitoring model, and outputting the first risk coefficient, wherein the multivariate monitoring model is connected in communication with the first construction simulation model; and outputting the first reminder information according to the first risk coefficient. The technical problem that the accuracy of the construction safety monitoring and early warning for high bridge piers in the prior art is poor, resulting in poor construction safety monitoring and early warning effects for high bridge piers is solved.

[0004] It can be seen that at present, in the safety monitoring system for high pier construction of bridges, construction safety mainly focuses on structural stress, construction machinery safety and construction personnel behavior, while there is relatively little monitoring of dust pollution in the construction environment. Traditional dust control methods mainly rely on manual visual inspection and dust reduction measures at fixed time intervals, but these methods have many shortcomings. For example, construction workers usually manually turn on the spray dust reduction equipment when they find that there is a lot of visible dust, but at this time the dust has already spread in the air, posing a potential threat to personnel health and equipment. In addition, existing spray dust reduction equipment generally operates at a fixed time, and does not combine real-time dust concentration, wind direction, wind speed and other factors for intelligent regulation, resulting in low dust reduction efficiency, and may even cause excessive dust reduction, resulting in uneven humidity of construction materials, affecting the quality of concrete and other problems.

[0005] On the other hand, although some construction scenarios have begun to introduce dust sensors for concentration monitoring, due to the lack of a high-precision dust diffusion prediction model, it is difficult for the system to warn of dust accumulation risks in advance. For example, when there is an instantaneous strong wind in the construction area or multiple devices are operating at the same time, the dust diffusion path will change dramatically, and the existing monitoring system can only provide static dust concentration data, unable to predict the dust diffusion trend, and unable to provide targeted dust reduction optimization solutions. Therefore, the intelligence and dynamic level of dust concentration monitoring in the construction environment still needs to be improved. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a BIM-based bridge high pier construction safety monitoring method and system, which solves the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A BIM-based bridge high pier construction safety monitoring method comprises the following steps:

[0008] S1. Extract BIM data of the construction area based on the BIM model, calculate and obtain the layout position, and then deploy multiple multi-dimensional environmental sensors in the construction area of ​​the bridge pier to collect dust diffusion information during the construction process to form an environmental feature set T;

[0009] S2. Calculate and obtain the dust diffusion concentration based on the acquired environmental feature set T to form a dust concentration prediction vector P;

[0010] S3. After analyzing the obtained dust concentration prediction vector P, a dust reduction parameter set D of the dynamic dust reduction strategy is obtained;

[0011] S4, executing a dynamic dust reduction strategy of the dust reduction parameter set D, and obtaining a dust concentration error △D after a fixed period;

[0012] S5. Calculate the comprehensive dust score of the bridge high pier construction area based on the obtained dust concentration error △D, obtain the environmental safety index ZS, and compare it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D.

[0013] Preferably, S1 includes S11 and S12;

[0014] S11. Extract BIM data of the construction area through the BIM model, including the construction area boundary (L, W, H), extract the construction area boundary length L based on the obtained construction area boundary (L, W, H), obtain the sensor layout spacing ds after calculation, and layout a multidimensional sensor group in the bridge high pier construction area based on the obtained sensor layout spacing ds. The multidimensional sensor group includes a dust sensor, a wind speed and wind direction sensor, an equipment operation status sensor, and an air humidity sensor;

[0015] Among them, the construction equipment operation area includes the drilling machine and mixer equipment operation area; the dust sensor includes the dust PM10 sensor and the dust PM2.5 sensor;

[0016] The sensor layout spacing ds is obtained by the following calculation formula:

[0017] ;

[0018] Where N represents the preset number of sensors.

[0019] Preferably, S12, based on the deployed multi-dimensional sensor group, the dust concentration Dc(i), wind speed Ws(i), wind direction Wd(i), equipment operation status Sm(i) and air humidity Ah(i) of the construction area are collected by the i-th sensor to form the environmental data collected by the i-th sensor, and the environmental data is transmitted by LoRa wireless communication transmission mode, and the environmental feature set T is formed by integrating the environmental data of all sensors;

[0020] The equipment operation status Sm(i) specifically represents the binary value of the equipment start-stop operation status collected by the i-th equipment operation status sensor. The binary value is specifically represented by 1 indicating that the equipment is in operation, and 0 indicating that the equipment is in shutdown. At the same time, when the equipment operation status Sm(i) = 1, the load intensity of the equipment is recorded as the associated data of the equipment operation status Sm(i);

[0021] The dust concentration Dc(i) specifically represents the dust PM10 concentration and dust PM2.5 concentration collected by the i-th sensor;

[0022] The environmental feature set T is specifically .

[0023] Preferably, S2 includes S21;

[0024] S21. Based on the acquired environmental feature set T, the predicted dust diffusion concentration Kc of PM10 and PM2.5 of dust at the i-th sensor position in the construction area is calculated using the Gaussian diffusion model, and the predicted dust diffusion concentration Kc of PM10 and PM2.5 of dust at all sensor positions is integrated to form a dust concentration prediction vector P;

[0025] The dust concentration prediction vector P is specifically P={Kc(1, PM10), Kc(1, PM2.5), Kc(2, PM10), Kc(2, PM2.5), Kc(i, PM10), Kc(i, PM2.5), …, Kc(N, PM10), Kc(N, PM2.5)};

[0026] The predicted dust diffusion concentration Kc of dust PM10 is obtained by the following formula:

[0027] ;

[0028] Where Kc(i, PM10) represents the PM10 predicted dust diffusion concentration Kc at the i-th sensor area, E(PM10) represents the amount of PM10 dust released by the dust source, which is calculated and obtained by the equipment operation status Sm(i) collected by the i-th sensor, U represents the wind speed, which is obtained by the wind speed Ws(i) collected by the i-th sensor, and π represents a mathematical constant with a value of 3.14. (PM10) and (PM10) represents the diffusion coefficient of PM10, specifically the diffusion range of PM10 dust in the horizontal y direction and vertical z direction, which is calculated by the diffusion empirical formula. It represents the modified diffusion coordinate in the lateral y direction, specifically, the modified diffusion coordinate of dust diffusion along the wind direction Wd(i) of the i-th sensor area actually collected. It indicates the diffusion of PM10 dust in the horizontal y direction, exp indicates the exponential function, It indicates the diffusion of PM10 dust in the vertical z direction. H(PM10) indicates the dust height of PM10 dust released by construction equipment, specifically the height of the equipment.

[0029] The predicted dust diffusion concentration Kc of dust PM2.5 is obtained by the following formula:

[0030] ;

[0031] Where Kc(i, PM2.5) represents the PM2.5 predicted dust diffusion concentration Kc at the i-th sensor area, E(PM2.5) represents the amount of PM2.5 dust released by the dust source, which is calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor, and U represents the wind speed, which is obtained through the wind speed Ws(i) collected by the i-th sensor. (PM2.5) and (PM2.5) represents the diffusion coefficient of PM2.5, specifically the diffusion range of PM2.5 dust in the horizontal y direction and vertical z direction, which is calculated by the diffusion empirical formula. It indicates the diffusion of PM2.5 dust in the horizontal y direction, exp indicates the exponential function, Indicates the diffusion of PM2.5 dust in the vertical z direction. H(PM2.5) indicates the dust height of PM2.5 dust released by construction equipment, specifically the height of the equipment.

[0032] Among them, the dust amount E of dust PM10 and dust PM2.5 is obtained by the following calculation formula:

[0033] ;

[0034] Wherein, E(PM10, PM2.5) represents the dust amount E of dust PM10 and dust PM2.5, specifically represents the dust amount E(PM10) of dust PM10 and the dust amount E(PM2.5) of dust PM2.5, ek represents the equipment dust release coefficient of dust PM10 and dust PM2.5, specifically measured by empirical values, specifically the equipment dust release coefficient ek(PM10) of dust PM10 and the equipment dust release coefficient ek(PM2.5) of dust PM2.5, M represents the total number of construction equipment, Sm(j) represents the operating status of the jth equipment, and P(j) represents the power of the jth equipment;

[0035] Corrected diffusion coordinate in the lateral y direction Obtained through the following calculation formula:

[0036] ;

[0037] Wherein, sin represents the sine function, and cos represents the cosine function.

[0038] Preferably, S3 includes S31 and S32;

[0039] S31. Perform dust reduction demand analysis based on the obtained dust concentration prediction vector P. The dust reduction demand analysis compares the distribution of the predicted dust diffusion concentration Kc in the construction area with the dust reduction threshold DT to obtain whether the i-th sensor area is to be dusted. By counting the areas that need to be dusted, a dust concentration prediction vector P for the areas with dust reduction demand is formed.

[0040] The dust fall threshold DT is specifically DT={DTpm10, DTpm2.5}, DTpm10 represents the dust fall safety threshold of PM10, and DTpm2.5 represents the dust fall safety threshold of PM2.5;

[0041] The construction area is obtained by comparing:

[0042] When the dust concentration prediction vector P (i, PM10) ≥ DTpm10, or the dust concentration prediction vector P (i, PM2.5) ≥ DTpm2.5, the dust in the i-th sensor area exceeds the standard and dust reduction treatment is required;

[0043] When the dust concentration prediction vector P (i, PM10) < DTpm10, and the dust concentration prediction vector P (i, PM2.5) < DTpm2.5, the dust in the i-th sensor area does not exceed the standard, and dust reduction treatment is not required;

[0044] Among them, the dust concentration prediction vector P (i, PM10) and the dust concentration prediction vector P (i, PM2.5) respectively represent the dust PM10 predicted dust diffusion concentration KcPM10 and the dust PM2.5 predicted dust diffusion concentration KcPM2.5 at the i-th sensor area in the dust concentration prediction vector P.

[0045] Preferably, S32, according to the dust concentration prediction vector P obtained in the area with dust reduction demand, the dust reduction parameters of the spray system are calculated, and the dynamic dust reduction strategy for each area that needs to be treated with dust reduction is obtained, and the dynamic dust reduction strategy includes the spray flow rate Q and dust reduction time T of dust PM10 and dust PM2.5, wherein the maximum dust value between dust PM10 and dust PM2.5 is obtained by comparing dust PM10 and dust PM2.5 with the dust reduction threshold DT, and the spray flow rate Q is calculated by the dust maximum value, and the dynamic dust reduction strategy of each area is integrated to form a dust reduction parameter set D;

[0046] The spray flow rate Q is obtained by the following calculation formula:

[0047] ;

[0048] Wherein, Q(i) represents the spray flow rate at the i-th sensor area, including the spray flow rate of dust PM10 and dust PM2.5, Qmax represents the maximum spray flow rate of the spray equipment, P(max, PM10) and P(max, PM2.5) represent the maximum values ​​of the predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5 in the dust concentration prediction vector P, respectively, and MAX represents the maximum value function, which is specifically used to return the maximum value of the calculation results of dust PM10 and dust PM2.5;

[0049] The dust fall time T includes the dust PM10 fall time T (i, PM10) and the dust PM2.5 fall time T (i, PM2.5);

[0050] The dust PM10 fall time T (i, PM10) is obtained by the following calculation formula:

[0051] ;

[0052] Where, T(i, PM10) represents the dust settling time of dust PM10 in the i-th sensor area, S(PM10) represents the natural settling rate of dust PM10, Ah(i) represents the air humidity in the i-th sensor area, kh represents the humidity correction coefficient, and Ahref represents the reference air humidity;

[0053] The dust PM2.5 settling time T (i, PM2.5) is obtained by the following calculation formula:

[0054] ;

[0055] Where T(i, PM2.5) represents the dust settling time of dust PM2.5 at the i-th sensor area, and S(PM2.5) represents the natural settling rate of dust PM2.5.

[0056] Preferably, S4 includes S41;

[0057] S41. Extract each sensor position in the dust reduction parameter set D according to the obtained dust reduction parameter set D, and execute the dynamic dust reduction strategy for each sensor position by controlling the spraying equipment, and obtain the dust concentration Dc(i) at the i-th sensor after a fixed period, and then compare it with the dust concentration Dc(i) at the i-th sensor obtained before executing the dynamic dust reduction strategy to obtain the dust concentration error △D of dust PM10 and dust PM2.5;

[0058] The dust concentration error △D (PM10) of dust PM10 is obtained by the following calculation formula:

[0059] ;

[0060] Wherein, the dust concentration error △D(i, PM10) represents the dust concentration error △D(i, PM10) of the dust PM10 at the i-th sensor, Dc(i, PM10, be) represents the dust concentration of the dust PM10 before the dynamic dust reduction strategy is implemented at the i-th sensor, and Dc(i, PM10, af) represents the dust concentration of the dust PM10 after the dynamic dust reduction strategy is implemented at the i-th sensor;

[0061] The dust concentration error △D (PM2.5) of dust PM2.5 is obtained by the following calculation formula:

[0062] ;

[0063] In the formula, the dust concentration error △D(i, PM2.5) represents the dust concentration error △D(i, PM2.5) of the dust PM2.5 at the i-th sensor, Dc(i, PM2.5, be) represents the dust concentration of PM2.5 before the dynamic dust reduction strategy is implemented at the i-th sensor, and Dc(i, PM2.5, af) represents the dust concentration of PM2.5 after the dynamic dust reduction strategy is implemented at the i-th sensor.

[0064] Preferably, S5 includes S51;

[0065] S51, based on the dust concentration error △D of the obtained dust PM10 and dust PM2.5, calculate the comprehensive dust score of the bridge high pier construction area, obtain the environmental safety index ZS, and compare it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D;

[0066] The environmental safety index ZS is obtained by the following calculation formula:

[0067] ;

[0068] In the formula, wpm10 and wpm2.5 represent the preset weight values ​​of dust PM10 and dust PM2.5 respectively, and the specific values ​​are set by experience.

[0069] Preferably, the trigger result is obtained by the following comparison method:

[0070] When the environmental safety index ZS ≥ the construction area trigger threshold ST, the trigger result of the iterative optimization of the dust reduction parameter set D is obtained as a non-trigger result;

[0071] When the environmental safety index ZS is less than the construction area trigger threshold ST, the trigger result of the iterative optimization of the dust reduction parameter set D is obtained as the trigger result, and the spray flow rate Q and the dust reduction time T are iteratively optimized.

[0072] The BIM-based bridge high pier construction safety monitoring system includes BIM model data extraction module, prediction vector generation module, dust fall analysis module, execution and monitoring module and comprehensive evaluation module;

[0073] The BIM model data extraction module extracts the BIM data of the construction area based on the BIM model, calculates and obtains the layout position, and then deploys multiple multi-dimensional environmental sensors in the bridge high pier construction area to collect dust diffusion information during the construction process to form an environmental feature set T;

[0074] The prediction vector generation module calculates the dust diffusion concentration based on the acquired environmental feature set T to form a dust concentration prediction vector P;

[0075] The dust reduction analysis module obtains the dust reduction parameter set D of the dynamic dust reduction strategy by analyzing the obtained dust concentration prediction vector P;

[0076] The execution and monitoring module executes the dynamic dust reduction strategy of the dust reduction parameter set D and obtains the dust concentration error △D after a fixed period;

[0077] The comprehensive evaluation module calculates the comprehensive dust score of the bridge high pier construction area based on the obtained dust concentration error △D, obtains the environmental safety index ZS, and compares it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D.

[0078] The present invention provides a BIM-based bridge high pier construction safety monitoring method and system, which has the following beneficial effects:

[0079] (1) The dust concentration prediction vector P is calculated by the environmental feature set T, which realizes the high-precision prediction of dust diffusion concentration and makes the dust distribution at the construction site more forward-looking. Then, this scheme combines the dynamic changes of dust concentration to formulate the dust reduction parameter set D and implements the dynamic dust reduction strategy. It can adjust the spraying scheme according to the real-time environment, effectively improve the dust reduction efficiency, and avoid the waste of water resources and uneven coverage problems existing in the traditional spraying method. In addition, through the feedback mechanism of the dust concentration error △D, the environmental safety index ZS is calculated and compared with the preset construction area trigger threshold ST to achieve iterative optimization of the dust reduction parameter set D, ensuring that the construction environment is always within the controllable range and avoiding safety hazards caused by excessive dust. Compared with the existing technology, this scheme can not only improve the accuracy of dust monitoring, but also adaptively adjust the dust reduction scheme through dynamic optimization strategy, greatly improve the intelligent level of air quality management in the construction area, and provide a more scientific, efficient and energy-saving dust control scheme for the safety of high pier construction of bridges.

[0080] (2) The dust concentration prediction vector P is formed by integrating the data of all sensor positions, making the simulation of dust diffusion more accurate and scientific. Compared with the traditional method that relies on single-point monitoring, the dust concentration prediction vector P is compared with the dust reduction threshold DT to accurately lock the dust concentration prediction vector P in the area with dust reduction demand, thereby avoiding unnecessary dust reduction operations and improving the response efficiency and accuracy of the spray system. Based on this, the spray flow Q and dust reduction time T are further calculated, and the maximum value of the predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5 is dynamically adjusted, so that the dust reduction strategy can optimize the allocation of water resources according to the real-time environment, ensuring the dust reduction effect while reducing water resource waste. In addition, considering the sedimentation characteristics of dust of different particle sizes, the spray time is corrected to make the dust reduction scheme more intelligent and adaptive. Compared with the traditional construction dust reduction method, this scheme can not only accurately predict the dust diffusion situation, but also dynamically adjust the spray strategy to ensure the reasonable allocation of dust reduction resources, effectively improve the quality of the construction environment, and ensure construction safety.

[0081] (3) The dust reduction parameter set D is used to accurately control the spray equipment at each sensor location, and the dust concentration error △D of PM10 and PM2.5 is dynamically obtained after a fixed period to ensure that the dust reduction effect in the construction area can be quantitatively evaluated and continuously optimized. Compared with the fixed dust reduction strategy, this solution can not only achieve quantitative evaluation of the dust reduction effect, but also adaptively adjust the spray flow Q and dust reduction time T based on the actual dust reduction effect, ensuring automatic optimization when the dust reduction effect does not meet the standard, so that the construction area is always kept within the dust safety threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a schematic diagram of the steps of the bridge high pier construction safety monitoring method based on BIM of the present invention;

[0083] Figure 2 It is a schematic diagram of a block diagram of a bridge high pier construction safety monitoring system based on BIM of the present invention;

[0084] Figure 3 This is the changing trend of PM10 and PM2.5 dust concentration. DETAILED DESCRIPTION

[0085] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0086] Example 1

[0087] The present invention provides a BIM-based bridge high pier construction safety monitoring method and system, please refer to Figure 1 , including the following steps:

[0088] S1. Extract BIM data of the construction area based on the BIM model, calculate and obtain the layout position, and then deploy multiple multi-dimensional environmental sensors in the construction area of ​​the bridge pier to collect dust diffusion information during the construction process to form an environmental feature set T;

[0089] S2. Calculate and obtain the dust diffusion concentration based on the acquired environmental feature set T to form a dust concentration prediction vector P;

[0090] S3. After analyzing the obtained dust concentration prediction vector P, a dust reduction parameter set D of the dynamic dust reduction strategy is obtained;

[0091] S4, executing a dynamic dust reduction strategy of the dust reduction parameter set D, and obtaining a dust concentration error △D after a fixed period;

[0092] S5. Calculate the comprehensive dust score of the bridge high pier construction area based on the obtained dust concentration error △D, obtain the environmental safety index ZS, and compare it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D.

[0093] In this embodiment, by extracting the BIM data of the construction area and optimizing the layout of multi-dimensional environmental sensors, the construction environment monitoring is made more comprehensive and the accuracy of data collection is improved. Secondly, the dust concentration prediction vector P is calculated through the environmental feature set T, which realizes the high-precision prediction of the dust diffusion concentration and makes the dust distribution at the construction site more forward-looking. Then, this scheme combines the dynamic changes of dust concentration to formulate a dust reduction parameter set D and implement a dynamic dust reduction strategy. It can adjust the spraying scheme according to the real-time environment, effectively improve the dust reduction efficiency, and avoid the waste of water resources and uneven coverage problems existing in traditional spraying methods. In addition, through the feedback mechanism of the dust concentration error △D, the environmental safety index ZS is calculated and compared with the preset construction area trigger threshold ST to achieve iterative optimization of the dust reduction parameter set D, ensuring that the construction environment is always within a controllable range and avoiding safety hazards caused by excessive dust. Compared with the existing technology, this solution can not only improve the accuracy of dust monitoring, but also adaptively adjust the dust reduction plan through dynamic optimization strategy, greatly improve the intelligent level of air quality management in the construction area, and provide a more scientific, efficient and energy-saving dust control solution for the safety of high pier construction of bridges.

[0094] Example 2

[0095] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S1 includes S11 and S12;

[0096] S11. Extract BIM data of the construction area through the BIM model, including the construction area boundary (L, W, H), extract the construction area boundary length L based on the obtained construction area boundary (L, W, H), obtain the sensor layout spacing ds after calculation, and layout a multidimensional sensor group in the bridge high pier construction area based on the obtained sensor layout spacing ds. The multidimensional sensor group includes a dust sensor, a wind speed and wind direction sensor, an equipment operation status sensor, and an air humidity sensor;

[0097] Among them, the construction equipment operation area includes the drilling machine and mixer equipment operation area; the dust sensor includes the dust PM10 sensor and the dust PM2.5 sensor;

[0098] The sensor layout spacing ds is obtained by the following calculation formula:

[0099] ;

[0100] Where N represents the preset number of sensors.

[0101] S12, based on the deployed multi-dimensional sensor group, the dust concentration Dc (i), wind speed Ws (i), wind direction Wd (i), equipment operation status Sm (i) and air humidity Ah (i) in the construction area are collected by the i-th sensor to form the environmental data collected by the i-th sensor, and the environmental data is transmitted by LoRa wireless communication transmission mode, and the environmental feature set T is formed by integrating the environmental data of all sensors;

[0102] The equipment operation status Sm(i) specifically represents the binary value of the equipment start-stop operation status collected by the i-th equipment operation status sensor. The binary value is specifically represented by 1 indicating that the equipment is in operation, and 0 indicating that the equipment is in shutdown. At the same time, when the equipment operation status Sm(i) = 1, the load intensity of the equipment is recorded as the associated data of the equipment operation status Sm(i);

[0103] The dust concentration Dc(i) specifically represents the dust PM10 concentration and dust PM2.5 concentration collected by the i-th sensor;

[0104] The environmental feature set T is specifically .

[0105] In this embodiment, the sensor layout spacing ds is calculated by comprehensively considering the construction area boundary (L, W, H), so as to realize the accurate layout and optimized distribution of the sensor group, making the construction environment monitoring more systematic and scientific. Through the multi-dimensional sensor group composed of dust sensors, wind speed and wind direction sensors, equipment operation status sensors and air humidity sensors, it is not only possible to comprehensively monitor the dust diffusion in the construction area, but also to collect wind speed Ws (i), wind direction Wd (i), equipment operation status Sm (i) and air humidity Ah (i) in real time. The LoRa wireless communication transmission method is adopted to ensure the low power consumption, high stability and long-distance coverage of the data transmission of the environmental feature set T, and the real-time and reliability of data transmission can be guaranteed even in large construction areas. Provide more targeted input data for the subsequent calculation of the dust concentration prediction vector P. Compared with the traditional construction environment monitoring method, it ensures the real-time and high reliability of dust diffusion monitoring in the construction area, and provides a more efficient, refined and intelligent environmental perception solution for the safety monitoring of high pier construction of bridges.

[0106] Example 3

[0107] This embodiment is explained in Example 2. Please refer to Figure 1 and Figure 3 , specifically: S2 includes S21;

[0108] S21. Based on the acquired environmental feature set T, the predicted dust diffusion concentration Kc of PM10 and PM2.5 of dust at the i-th sensor position in the construction area is calculated using the Gaussian diffusion model, and the predicted dust diffusion concentration Kc of PM10 and PM2.5 of dust at all sensor positions is integrated to form a dust concentration prediction vector P;

[0109] The dust concentration prediction vector P is specifically P={Kc(1, PM10), Kc(1, PM2.5), Kc(2, PM10), Kc(2, PM2.5), Kc(i, PM10), Kc(i, PM2.5), …, Kc(N, PM10), Kc(N, PM2.5)};

[0110] The predicted dust diffusion concentration Kc of dust PM10 is obtained by the following formula:

[0111] ;

[0112] Where Kc(i, PM10) represents the PM10 predicted dust diffusion concentration Kc at the i-th sensor area, E(PM10) represents the amount of PM10 dust released by the dust source, which is calculated and obtained by the equipment operation status Sm(i) collected by the i-th sensor, U represents the wind speed, which is obtained by the wind speed Ws(i) collected by the i-th sensor, and π represents a mathematical constant with a value of 3.14. (PM10) and (PM10) represents the diffusion coefficient of PM10, specifically the diffusion range of PM10 dust in the horizontal y direction and vertical z direction, which is calculated by the diffusion empirical formula. It represents the modified diffusion coordinate in the lateral y direction, specifically, the modified diffusion coordinate of dust diffusion along the wind direction Wd(i) of the i-th sensor area actually collected. This term is the normalization factor in the formula for predicting dust diffusion concentration Kc, which is used to ensure that the total amount of pollutants remains consistent and correctly simulate the diffusion process. It indicates the diffusion of PM10 dust in the horizontal y direction. exp represents an exponential function, which means that the farther the horizontal y direction is, the lower the dust concentration is. It indicates the diffusion of PM10 dust in the vertical z direction. H(PM10) indicates the dust height of PM10 dust released by construction equipment. Specifically, it indicates the height of equipment. Specifically, the closer to the pollution source height H, the higher the concentration, and the farther away, the lower the concentration;

[0113] The predicted dust diffusion concentration Kc of dust PM2.5 is obtained by the following formula:

[0114] ;

[0115] Where Kc(i, PM2.5) represents the PM2.5 predicted dust diffusion concentration Kc at the i-th sensor area, E(PM2.5) represents the amount of PM2.5 dust released by the dust source, which is calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor, and U represents the wind speed, which is obtained through the wind speed Ws(i) collected by the i-th sensor. (PM2.5) and (PM2.5) represents the diffusion coefficient of PM2.5, specifically the diffusion range of PM2.5 dust in the horizontal y direction and vertical z direction, which is calculated by the diffusion empirical formula. This term is the normalization factor in the formula for predicting dust diffusion concentration Kc, which is used to ensure that the total amount of pollutants remains consistent and correctly simulate the diffusion process. It indicates the diffusion of PM2.5 dust in the horizontal y direction. exp represents an exponential function, which means that the farther the horizontal y direction is, the lower the dust concentration is. Indicates the diffusion of PM2.5 dust in the vertical z direction. H(PM2.5) indicates the dust height of PM2.5 dust released by construction equipment. Specifically, it indicates the height of the equipment. Specifically, the closer to the pollution source height H, the higher the concentration, and the farther away, the lower the concentration;

[0116] Among them, the dust amount E of dust PM10 and dust PM2.5 is obtained by the following calculation formula:

[0117] ;

[0118] Wherein, E(PM10, PM2.5) represents the dust amount E of PM10 and PM2.5, specifically represents the dust amount E(PM10) of PM10 and the dust amount E(PM2.5) of PM2.5, ek represents the equipment dust release coefficient of PM10 and PM2.5, specifically measured by empirical values, specifically the equipment dust release coefficient ek(PM10) of PM10 and the equipment dust release coefficient ek(PM2.5) of PM2.5, M represents the total number of construction equipment, Sm(j) represents the operating status of the jth equipment, the jth equipment operating status Sm(j)=1 represents that the equipment is in operation and releases dust, the jth equipment operating status Sm(j)=0 represents that the equipment is in shutdown and does not release dust, and P(j) represents the power of the jth equipment;

[0119] For example: the equipment dust release coefficient of PM10 is ek=0.5, and the operating power of the equipment is 10kW, then the dust amount of PM10 is E=0.5*10=5mg / s;

[0120] The dust height H is obtained by extracting the geometric information of the equipment from the BIM model. The equipment height is extracted from the geometric information and marked as the dust height H.

[0121] Corrected diffusion coordinate in the lateral y direction Obtained through the following calculation formula:

[0122] ;

[0123] In the formula, sin represents the sine function, cos represents the cosine function, It indicates the projection of the dust component in the vertical axis x direction on the wind direction Wd. It represents the projection of the dust component in the horizontal axis y direction on the wind direction Wd.

[0124] S3 includes S31 and S32;

[0125] S31. Perform dust reduction demand analysis based on the obtained dust concentration prediction vector P. The dust reduction demand analysis compares the distribution of the predicted dust diffusion concentration Kc in the construction area with the dust reduction threshold DT to obtain whether the i-th sensor area is to be dusted. By counting the areas that need to be dusted, a dust concentration prediction vector P for the areas with dust reduction demand is formed.

[0126] The dust fall threshold DT is specifically DT={DTpm10, DTpm2.5}, DTpm10 represents the dust fall safety threshold of PM10, and DTpm2.5 represents the dust fall safety threshold of PM2.5;

[0127] The construction area is obtained by comparing:

[0128] When the dust concentration prediction vector P (i, PM10) ≥ DTpm10, or the dust concentration prediction vector P (i, PM2.5) ≥ DTpm2.5, the dust in the i-th sensor area exceeds the standard and dust reduction treatment is required;

[0129] When the dust concentration prediction vector P (i, PM10) < DTpm10, and the dust concentration prediction vector P (i, PM2.5) < DTpm2.5, the dust in the i-th sensor area does not exceed the standard, and dust reduction treatment is not required;

[0130] Among them, the dust concentration prediction vector P (i, PM10) and the dust concentration prediction vector P (i, PM2.5) respectively represent the dust PM10 predicted dust diffusion concentration KcPM10 and the dust PM2.5 predicted dust diffusion concentration KcPM2.5 at the i-th sensor area in the dust concentration prediction vector P.

[0131] S32. Calculate the dust reduction parameters of the spray system according to the dust concentration prediction vector P obtained for the area with dust reduction demand, and obtain the dynamic dust reduction strategy for each area that needs to be treated with dust reduction. The dynamic dust reduction strategy includes the spray flow rate Q and dust reduction time T of dust PM10 and dust PM2.5. The maximum dust value between dust PM10 and dust PM2.5 is obtained by comparing dust PM10 and dust PM2.5 with the dust reduction threshold DT, and the spray flow rate Q is calculated by the dust maximum value. The dynamic dust reduction strategy of each area is integrated to form a dust reduction parameter set D.

[0132] The spray flow rate Q is obtained by the following calculation formula:

[0133] ;

[0134] Wherein, Q(i) represents the spray flow rate at the i-th sensor area, including the spray flow rate of dust PM10 and dust PM2.5, Qmax represents the maximum spray flow rate of the spray equipment, P(max, PM10) and P(max, PM2.5) represent the maximum values ​​of the predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5 in the dust concentration prediction vector P, respectively, and MAX represents the maximum value function, which is specifically used to return the maximum value of the calculation results of dust PM10 and dust PM2.5;

[0135] The dust fall time T includes the dust PM10 fall time T (i, PM10) and the dust PM2.5 fall time T (i, PM2.5);

[0136] The dust PM10 fall time T (i, PM10) is obtained by the following calculation formula:

[0137] ;

[0138] Wherein, T(i, PM10) represents the dust settling time of PM10 dust at the i-th sensor area, S(PM10) represents the natural settling rate of PM10 dust, Ah(i) represents the air humidity at the i-th sensor area, kh represents the humidity correction coefficient, which is used to consider the effect of air humidity on the dust settling rate. When the humidity is high, dust particles are easy to absorb moisture, resulting in larger particles and faster settling rate. Therefore, the dust settling time needs to be adjusted, which is set by the user. Ahref represents the reference air humidity, which is used to measure and standardize the difference between the current air humidity and the reference humidity. It is also for the purpose of standardizing the actual air humidity (Ah) and adjusting the effect of humidity on the dust settling rate. If the current humidity Ah is high, the settling rate of dust particles is fast, so the dust settling time needs to be corrected. If the humidity is low, the settling rate slows down, and the dust settling time needs to be extended.

[0139] The dust PM2.5 settling time T (i, PM2.5) is obtained by the following calculation formula:

[0140] ;

[0141] Where T(i, PM2.5) represents the dust settling time of dust PM2.5 at the i-th sensor area, and S(PM2.5) represents the natural settling rate of dust PM2.5.

[0142] In this embodiment, based on the environmental feature set T, the Gaussian diffusion model is used to calculate the predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5, and the dust concentration prediction vector P is formed by integrating the data of all sensor positions, so that the simulation of dust diffusion is more accurate and scientific. Compared with the traditional method that relies on single-point monitoring, this solution can dynamically predict dust diffusion through multi-dimensional environmental parameters such as the operating status of construction equipment Sm (i), wind speed Ws (i), wind direction Wd (i) and air humidity Ah (i), ensuring that the temporal and spatial variation trend of dust concentration is accurately reflected. In addition, this solution compares the dust concentration prediction vector P with the dust reduction threshold DT to accurately lock the dust concentration prediction vector P in the area with dust reduction demand, thereby avoiding unnecessary dust reduction operations and improving the response efficiency and accuracy of the sprinkler system. Based on this, this solution further calculates the spray flow Q and dust reduction time T, and dynamically adjusts the maximum value of the predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5, so that the dust reduction strategy can optimize the allocation of water resources according to the real-time environment, ensuring the dust reduction effect while reducing water resource waste. In addition, considering the sedimentation characteristics of dust particles of different particle sizes, this solution calculates the dust reduction time T (i, PM10) of dust PM10 and the dust reduction time T (i, PM2.5) of dust PM2.5 respectively, and corrects the spray time in combination with the air humidity Ah (i), making the dust reduction scheme more intelligent and adaptive. Compared with traditional construction dust reduction methods, this solution can not only accurately predict the dust diffusion situation, but also dynamically adjust the spray strategy to ensure the reasonable allocation of dust reduction resources, and ultimately improve the accuracy, response speed and dust reduction efficiency of dust management in the construction area, effectively improve the quality of the construction environment, and ensure construction safety.

[0143] Example 4

[0144] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S4 includes S41;

[0145] S41. Extract each sensor position in the dust reduction parameter set D according to the obtained dust reduction parameter set D, and execute the dynamic dust reduction strategy for each sensor position by controlling the spraying equipment, and obtain the dust concentration Dc(i) at the i-th sensor after a fixed period, and then compare it with the dust concentration Dc(i) at the i-th sensor obtained before executing the dynamic dust reduction strategy to obtain the dust concentration error △D of dust PM10 and dust PM2.5;

[0146] The dust concentration error △D (PM10) of dust PM10 is obtained by the following calculation formula:

[0147] ;

[0148] Wherein, the dust concentration error △D(i, PM10) represents the dust concentration error △D(i, PM10) of the dust PM10 at the i-th sensor, Dc(i, PM10, be) represents the dust concentration of the dust PM10 before the dynamic dust reduction strategy is implemented at the i-th sensor, and Dc(i, PM10, af) represents the dust concentration of the dust PM10 after the dynamic dust reduction strategy is implemented at the i-th sensor;

[0149] The dust concentration error △D (PM2.5) of dust PM2.5 is obtained by the following calculation formula:

[0150] ;

[0151] In the formula, the dust concentration error △D(i, PM2.5) represents the dust concentration error △D(i, PM2.5) of the dust PM2.5 at the i-th sensor, Dc(i, PM2.5, be) represents the dust concentration of PM2.5 before the dynamic dust reduction strategy is implemented at the i-th sensor, and Dc(i, PM2.5, af) represents the dust concentration of PM2.5 after the dynamic dust reduction strategy is implemented at the i-th sensor.

[0152] S5 includes S51;

[0153] S51, based on the dust concentration error △D of the obtained dust PM10 and dust PM2.5, calculate the comprehensive dust score of the bridge high pier construction area, obtain the environmental safety index ZS, and compare it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D;

[0154] The environmental safety index ZS is obtained by the following calculation formula:

[0155] ;

[0156] Wherein, wpm10 and wpm2.5 represent the preset weight values ​​of dust PM10 and dust PM2.5 respectively, the specific values ​​are set by experience, and N represents the preset number of sensors.

[0157] Example of obtaining the environmental safety index ZS:

[0158] Number of sensors N=3;

[0159] Dust concentration error △D(i, PM2.5) of dust PM2.5 at the i-th sensor: 40, 30, 35;

[0160] Dust concentration error △D(i, PM10) of dust PM2.5 at the i-th sensor: 20, 25, 22;

[0161] The maximum value of the predicted dust diffusion concentration Kc of dust PM10 in the dust concentration prediction vector P is 250;

[0162] The maximum value of the predicted dust diffusion concentration Kc of dust PM10 in the dust concentration prediction vector P is 150;

[0163] The preset weight value of dust PM10 is 0.5;

[0164] The preset weight value of dust PM2.5 is 0.5;

[0165] Construction area trigger threshold ST: 0.85;

[0166] Calculate the mean PM10 error:

[0167] 35=(40+30+35) / 3=35;

[0168] And normalized: 35 / 250=0.14;

[0169] Calculate the mean PM2.5 error:

[0170] 22.33=(20+25+22) / 3;

[0171] And normalized: 22.33 / 150=0.149;

[0172] According to the calculation formula of environmental safety index ZS:

[0173] Zs=1−(0.5×0.14+0.5×0.149)=1−0.1445=0.8555;

[0174] Compare the environmental safety index ZS and the construction area trigger threshold ST to obtain the trigger result:

[0175] When the environmental safety index ZS (0.855) ≥ the construction area trigger threshold ST (0.85), the trigger result of the iterative optimization of the dust reduction parameter set D is a non-trigger result;

[0176] The trigger result is obtained by the following comparison method:

[0177] When the environmental safety index ZS ≥ the construction area trigger threshold ST, the trigger result of the iterative optimization of the dust reduction parameter set D is obtained as a non-trigger result;

[0178] When the environmental safety index ZS is less than the construction area trigger threshold ST, the trigger result of the iterative optimization of the dust reduction parameter set D is obtained as the trigger result, and the spray flow rate Q and the dust reduction time T are iteratively optimized.

[0179] In this embodiment, the spray equipment at each sensor position is precisely controlled by the dust reduction parameter set D, and the dust concentration error △D of dust PM10 and dust PM2.5 is dynamically obtained after a fixed period, ensuring that the dust reduction effect of the construction area can be quantitatively evaluated and continuously optimized. Compared with the traditional construction dust reduction method that relies on fixed spraying time and empirical estimation, this solution can compare the dust PM10 dust concentration Dc (i, PM10, be) and dust PM2.5 dust concentration Dc (i, PM2.5, be) before and after the dynamic dust reduction strategy is executed at the i-th sensor in real time, thereby accurately calculating the dust concentration error △D (i, PM10) of dust PM10 and the dust concentration error △D (i, PM2.5) of dust PM2.5, ensuring that the dust concentration reduction at each monitoring point can be quantitatively monitored. Based on this, this solution further calculates the environmental safety index ZS and compares it with the construction area trigger threshold ST, so that the dust reduction system can intelligently determine whether it is necessary to iteratively optimize the dust reduction parameter set D. In particular, this solution combines the preset weight values ​​wpm10 and wpm2.5 of dust PM10 and dust PM2.5 with the influence weights of different dust particles, so that the calculation of the environmental safety index ZS is more in line with the actual air quality evaluation standards of the construction site. Compared with the fixed dust reduction strategy, this solution can not only realize the quantitative evaluation of the dust reduction effect, but also adaptively adjust the spray flow Q and dust reduction time T based on the actual dust reduction effect, ensuring automatic optimization when the dust reduction effect does not meet the standard, so that the construction area always remains within the dust safety threshold. Finally, this solution constructs an efficient, accurate, and sustainably optimized intelligent dust monitoring and dust reduction management system for bridge high pier construction areas through feedback control and iterative optimization mechanisms, which greatly improves the air quality of the construction environment, ensures the health and safety of construction personnel, and optimizes the efficiency of water resource use.

[0180] Example 5

[0181] For the BIM-based bridge high pier construction safety monitoring method and system, please refer to Figure 2 ,Specifically: including BIM model data extraction module, prediction vector generation module, dust fall analysis module, execution and monitoring module and comprehensive evaluation module;

[0182] The BIM model data extraction module extracts the BIM data of the construction area based on the BIM model, calculates and obtains the layout position, and then deploys multiple multi-dimensional environmental sensors in the bridge high pier construction area to collect dust diffusion information during the construction process to form an environmental feature set T;

[0183] The prediction vector generation module calculates the dust diffusion concentration based on the acquired environmental feature set T to form a dust concentration prediction vector P;

[0184] The dust reduction analysis module obtains the dust reduction parameter set D of the dynamic dust reduction strategy by analyzing the obtained dust concentration prediction vector P;

[0185] The execution and monitoring module executes the dynamic dust reduction strategy of the dust reduction parameter set D and obtains the dust concentration error △D after a fixed period;

[0186] The comprehensive evaluation module calculates the comprehensive dust score of the bridge high pier construction area based on the obtained dust concentration error △D, obtains the environmental safety index ZS, and compares it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D.

[0187] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A BIM-based bridge high pier construction safety monitoring method, characterized by: The following steps are involved: S1. Extract BIM data of the construction area based on the BIM model, calculate and obtain the layout position, and then deploy multiple multi-dimensional environmental sensors in the construction area of ​​the bridge pier to collect dust diffusion information during the construction process to form an environmental feature set T; S2, calculating and obtaining dust diffusion concentration based on the acquired environmental feature set T, and forming a dust concentration prediction vector P; S2 includes S21; S21. Based on the acquired environmental feature set T, the predicted dust diffusion concentration Kc of PM10 and PM2.5 of dust at the i-th sensor position in the construction area is calculated using the Gaussian diffusion model, and the predicted dust diffusion concentration Kc of PM10 and PM2.5 of dust at all sensor positions is integrated to form a dust concentration prediction vector P; S3. After analyzing the obtained dust concentration prediction vector P, a dust reduction parameter set D of the dynamic dust reduction strategy is obtained; S4, executing a dynamic dust reduction strategy of the dust reduction parameter set D, and obtaining a dust concentration error △D after a fixed period; S5. Calculate the dust comprehensive score of the bridge high pier construction area based on the obtained dust concentration error △D, obtain the environmental safety index ZS, and compare it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D; The trigger result is obtained by the following comparison method: When the environmental safety index ZS ≥ the construction area trigger threshold ST, the trigger result of the iterative optimization of the dust reduction parameter set D is obtained as a non-trigger result; When the environmental safety index ZS is less than the construction area trigger threshold ST, the trigger result of the iterative optimization of the dust reduction parameter set D is obtained as the trigger result, and the spray flow rate Q and the dust reduction time T are iteratively optimized.

2. The BIM-based bridge high pier construction safety monitoring method according to claim 1 is characterized by: S1 includes S11 and S12; S11. Extract BIM data of the construction area through the BIM model, including the construction area boundary (L, W, H), extract the construction area boundary length L based on the obtained construction area boundary (L, W, H), obtain the sensor layout spacing ds after calculation, and layout a multidimensional sensor group in the bridge high pier construction area based on the obtained sensor layout spacing ds. The multidimensional sensor group includes a dust sensor, a wind speed and wind direction sensor, an equipment operation status sensor, and an air humidity sensor; Among them, the construction equipment operation area includes the drilling machine and mixer equipment operation area; the dust sensor includes the dust PM10 sensor and the dust PM2.5 sensor; The sensor layout spacing ds is obtained by the following calculation formula: ； Where N represents the preset number of sensors.

3. The BIM-based bridge high pier construction safety monitoring method according to claim 2 is characterized by: S12, based on the deployed multi-dimensional sensor group, the dust concentration Dc (i), wind speed Ws (i), wind direction Wd (i), equipment operation status Sm (i) and air humidity Ah (i) in the construction area are collected by the i-th sensor to form the environmental data collected by the i-th sensor, and the environmental data is transmitted by LoRa wireless communication transmission mode, and the environmental feature set T is formed by integrating the environmental data of all sensors; The equipment operation status Sm(i) specifically represents the binary value of the equipment start-stop operation status collected by the i-th equipment operation status sensor. The binary value is specifically represented by 1 indicating that the equipment is in operation, and 0 indicating that the equipment is in shutdown. At the same time, when the equipment operation status Sm(i) = 1, the load intensity of the equipment is recorded as the associated data of the equipment operation status Sm(i); The dust concentration Dc(i) specifically represents the dust PM10 concentration and dust PM2.5 concentration collected by the i-th sensor; The environmental feature set T is specifically .

4. The BIM-based bridge high pier construction safety monitoring method according to claim 3 is characterized by: The dust concentration prediction vector P is specifically P={Kc(1, PM10), Kc(1, PM2.5), Kc(2, PM10), Kc(2, PM2.5), Kc(i, PM10), Kc(i, PM2.5), …, Kc(N, PM10), Kc(N, PM2.5)}; The predicted dust diffusion concentration Kc of dust PM10 is obtained by the following formula: ； Where Kc(i, PM10) represents the PM10 predicted dust diffusion concentration Kc at the i-th sensor area, E(PM10) represents the amount of PM10 dust released by the dust source, which is calculated and obtained by the equipment operation status Sm(i) collected by the i-th sensor, U represents the wind speed, which is obtained by the wind speed Ws(i) collected by the i-th sensor, and π represents a mathematical constant with a value of 3.

14. (PM10) and (PM10) represents the diffusion coefficient of PM10, specifically the diffusion range of PM10 dust in the horizontal y direction and vertical z direction, which is calculated by the diffusion empirical formula. It represents the modified diffusion coordinate in the lateral y direction, specifically, the modified diffusion coordinate of dust diffusion along the wind direction Wd(i) of the i-th sensor area actually collected. It indicates the diffusion of PM10 dust in the horizontal y direction, exp indicates the exponential function, It indicates the diffusion of PM10 dust in the vertical z direction, and H(PM10) indicates the dust height at which PM10 dust is released by construction equipment; The predicted dust diffusion concentration Kc of dust PM2.5 is obtained by the following formula: ； Where Kc(i, PM2.5) represents the PM2.5 predicted dust diffusion concentration Kc at the i-th sensor area, E(PM2.5) represents the amount of PM2.5 dust released by the dust source, which is calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor, and U represents the wind speed, which is obtained through the wind speed Ws(i) collected by the i-th sensor. (PM2.5) and (PM2.5) represents the diffusion coefficient of PM2.5, specifically the diffusion range of PM2.5 dust in the horizontal y direction and vertical z direction, which is calculated by the diffusion empirical formula. It indicates the diffusion of PM2.5 dust in the horizontal y direction, exp indicates the exponential function, It indicates the diffusion of PM2.5 dust in the vertical z direction, and H (PM2.5) indicates the dust height at which PM2.5 dust is released by construction equipment; Among them, the dust amount E of dust PM10 and dust PM2.5 is obtained by the following calculation formula: ； Wherein, E(PM10, PM2.5) represents the dust amount E of dust PM10 and dust PM2.5, specifically represents the dust amount E(PM10) of dust PM10 and the dust amount E(PM2.5) of dust PM2.5, ek represents the equipment dust release coefficient of dust PM10 and dust PM2.5, specifically measured by empirical values, specifically the equipment dust release coefficient ek(PM10) of dust PM10 and the equipment dust release coefficient ek(PM2.5) of dust PM2.5, M represents the total number of construction equipment, Sm(j) represents the operating status of the jth equipment, and P(j) represents the power of the jth equipment; Corrected diffusion coordinate in the lateral y direction Obtained through the following calculation formula: ； Wherein, sin represents the sine function, and cos represents the cosine function.

5. The BIM-based bridge high pier construction safety monitoring method according to claim 4 is characterized by: S3 includes S31 and S32; S31. Perform dust reduction demand analysis based on the obtained dust concentration prediction vector P. The dust reduction demand analysis compares the distribution of the predicted dust diffusion concentration Kc in the construction area with the dust reduction threshold DT to obtain whether the i-th sensor area is to be dusted. By counting the areas that need to be dusted, a dust concentration prediction vector P for the areas with dust reduction demand is formed. The dust fall threshold DT is specifically DT={DTpm10, DTpm2.5}, DTpm10 represents the dust fall safety threshold of PM10, and DTpm2.5 represents the dust fall safety threshold of PM2.5; The construction area is obtained by comparing: When the dust concentration prediction vector P (i, PM10) ≥ DTpm10, or the dust concentration prediction vector P (i, PM2.5) ≥ DTpm2.5, the dust in the i-th sensor area exceeds the standard and dust reduction treatment is required; When the dust concentration prediction vector P (i, PM10) < DTpm10, and the dust concentration prediction vector P (i, PM2.5) < DTpm2.5, the dust in the i-th sensor area does not exceed the standard, and dust reduction treatment is not required; Among them, the dust concentration prediction vector P (i, PM10) and the dust concentration prediction vector P (i, PM2.5) respectively represent the dust PM10 predicted dust diffusion concentration KcPM10 and the dust PM2.5 predicted dust diffusion concentration KcPM2.5 at the i-th sensor area in the dust concentration prediction vector P.

6. The BIM-based bridge high pier construction safety monitoring method according to claim 5 is characterized by: S32. Calculate the dust reduction parameters of the spray system according to the dust concentration prediction vector P obtained for the area with dust reduction demand, and obtain the dynamic dust reduction strategy for each area that needs to be treated with dust reduction. The dynamic dust reduction strategy includes the spray flow rate Q and dust reduction time T of dust PM10 and dust PM2.

5. The maximum dust value between dust PM10 and dust PM2.5 is obtained by comparing dust PM10 and dust PM2.5 with the dust reduction threshold DT, and the spray flow rate Q is calculated by the dust maximum value. The dynamic dust reduction strategy of each area is integrated to form a dust reduction parameter set D. The spray flow rate Q is obtained by the following calculation formula: ; Wherein, Q(i) represents the spray flow rate at the i-th sensor area, including the spray flow rate of dust PM10 and dust PM2.5, Qmax represents the maximum spray flow rate of the spray equipment, P(max, PM10) and P(max, PM2.5) represent the maximum values ​​of the predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5 in the dust concentration prediction vector P, respectively, and MAX represents the maximum value function, which is specifically used to return the maximum value of the calculation results of dust PM10 and dust PM2.5; The dust fall time T includes the dust PM10 fall time T (i, PM10) and the dust PM2.5 fall time T (i, PM2.5); The dust PM10 fall time T (i, PM10) is obtained by the following calculation formula: ； Where, T(i, PM10) represents the dust settling time of dust PM10 in the i-th sensor area, S(PM10) represents the natural settling rate of dust PM10, Ah(i) represents the air humidity in the i-th sensor area, kh represents the humidity correction coefficient, and Ahref represents the reference air humidity; The dust PM2.5 settling time T (i, PM2.5) is obtained by the following calculation formula: ； Where T(i, PM2.5) represents the dust settling time of dust PM2.5 at the i-th sensor area, and S(PM2.5) represents the natural settling rate of dust PM2.

5.

7. The BIM-based bridge high pier construction safety monitoring method according to claim 6 is characterized by: S4 includes S41; S41. Extract each sensor position in the dust reduction parameter set D according to the obtained dust reduction parameter set D, and execute the dynamic dust reduction strategy for each sensor position by controlling the spraying equipment, and obtain the dust concentration Dc(i) at the i-th sensor after a fixed period, and then compare it with the dust concentration Dc(i) at the i-th sensor obtained before executing the dynamic dust reduction strategy to obtain the dust concentration error △D of dust PM10 and dust PM2.5; The dust concentration error △D (PM10) of dust PM10 is obtained by the following calculation formula: ； Wherein, the dust concentration error △D(i, PM10) represents the dust concentration error △D(i, PM10) of the dust PM10 at the i-th sensor, Dc(i, PM10, be) represents the dust concentration of the dust PM10 before the dynamic dust reduction strategy is implemented at the i-th sensor, and Dc(i, PM10, af) represents the dust concentration of the dust PM10 after the dynamic dust reduction strategy is implemented at the i-th sensor; The dust concentration error △D (PM2.5) of dust PM2.5 is obtained by the following calculation formula: ； In the formula, the dust concentration error △D(i, PM2.5) represents the dust concentration error △D(i, PM2.5) of the dust PM2.5 at the i-th sensor, Dc(i, PM2.5, be) represents the dust concentration of PM2.5 before the dynamic dust reduction strategy is implemented at the i-th sensor, and Dc(i, PM2.5, af) represents the dust concentration of PM2.5 after the dynamic dust reduction strategy is implemented at the i-th sensor.

8. The BIM-based bridge high pier construction safety monitoring method according to claim 7 is characterized by: S5 includes S51; S51, based on the dust concentration error △D of the obtained dust PM10 and dust PM2.5, calculate the comprehensive dust score of the bridge high pier construction area, obtain the environmental safety index ZS, and compare it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D; The environmental safety index ZS is obtained by the following calculation formula: ； Wherein, wpm10 and wpm2.5 represent the preset weight values ​​of dust PM10 and dust PM2.5 respectively.

9. A BIM-based bridge high pier construction safety monitoring system, applied to a BIM-based bridge high pier construction safety monitoring method according to any one of claims 1 to 8, characterized in that: It includes BIM model data extraction module, prediction vector generation module, dust fall analysis module, execution and monitoring module and comprehensive evaluation module; The BIM model data extraction module extracts the BIM data of the construction area based on the BIM model, calculates and obtains the layout position, and then deploys multiple multi-dimensional environmental sensors in the bridge high pier construction area to collect dust diffusion information during the construction process to form an environmental feature set T; The prediction vector generation module calculates the dust diffusion concentration based on the acquired environmental feature set T to form a dust concentration prediction vector P; The dust reduction analysis module obtains the dust reduction parameter set D of the dynamic dust reduction strategy by analyzing the obtained dust concentration prediction vector P; The execution and monitoring module executes the dynamic dust reduction strategy of the dust reduction parameter set D and obtains the dust concentration error △D after a fixed period; The comprehensive evaluation module calculates the comprehensive dust score of the bridge high pier construction area based on the obtained dust concentration error △D, obtains the environmental safety index ZS, and compares it with the preset construction area trigger threshold ST to obtain the trigger result of the iterative optimization of the dust reduction parameter set D.

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