BIM-based Construction Safety Monitoring Method and System for High Bridge Piers

Abstract

The present invention discloses a method and system for safety monitoring of high piers in bridge construction based on BIM, which relates to the field of BIM technology. By calculating the dust concentration prediction vector P through the environmental feature set T, high-precision prediction of the dust diffusion concentration is achieved, making the dust distribution situation at the construction site more forward-looking. Then, combined with the dynamic changes in the dust concentration, a dust reduction parameter set D is formulated, and a dynamic dust reduction strategy is executed, which can adjust the spraying plan according to the real-time environment, effectively improving the dust reduction efficiency and avoiding the problems of water resource waste and uneven coverage 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 triggering threshold ST for the construction area to realize the iterative optimization of the dust reduction parameter set D, ensuring that the construction environment is always within a controllable range and avoiding potential safety hazards caused by excessive dust.

Description

Technical Field

[0001] The present invention relates to the field of BIM technology, and specifically to a method and system for safety monitoring of high bridge piers based on BIM. Background Art

[0002] In the field of modern infrastructure construction, bridge engineering, as an important part of the transportation network, its construction quality and safety directly affect the stability and sustainability of the entire transportation system. Especially in construction projects such as expressways, large urban interchanges, and cross-river and cross-sea bridges, the construction of high bridge piers has become a key object of concern for engineering safety monitoring due to its tall structure, complex stress environment, and high construction difficulty. BIM technology can integrate multi-dimensional information such as bridge design, construction, and maintenance, realizing visualization, parametric simulation, and risk warning during the construction process, thus greatly improving construction safety.

[0003] In the invention with the Chinese patent application number 202310860458.3, a method and system for safety monitoring of high bridge piers based on BIM are disclosed, which relate to the field of construction monitoring. Among them, the method includes: performing BIM simulation modeling on a first target bridge to output a first bridge simulation model; outputting a first geological simulation model; performing model fusion according to the first bridge simulation model and the first geological simulation model to output a first construction simulation model; performing construction simulation according to the first construction simulation model to obtain a first simulation data set; inputting the first simulation data set into a multi-monitoring model, and performing safety monitoring according to the multi-monitoring model to output a first risk coefficient, wherein the multi-monitoring model is communicatively connected to the first construction simulation model; and outputting a first reminder message according to the first risk coefficient. This solves the technical problem in the prior art that the accuracy of construction safety monitoring and early warning for high bridge piers is poor, resulting in a poor effect of construction safety monitoring and early warning for high bridge piers.

[0004] It can be seen that currently, in the construction safety monitoring system for high bridge piers, construction safety mainly focuses on structural stress, construction machinery safety, and construction personnel behavior, while relatively less attention is paid to dust pollution monitoring in the construction environment. Traditional dust control methods mainly rely on manual visual inspection and dust suppression measures at fixed time intervals, but these methods have many deficiencies. For example, construction workers usually manually turn on the spray dust suppression equipment when they find that there is more 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 suppression equipment generally operates at fixed times and does not perform intelligent regulation in combination with real-time dust concentration, wind direction, wind speed, and other factors, resulting in low dust suppression efficiency. It may even cause problems such as uneven humidity of construction materials due to excessive dust suppression, affecting the quality of concrete.

[0005] On the other hand, although dust sensors have been introduced in some construction scenarios for concentration monitoring, due to the lack of a high-precision dust diffusion prediction model, it is difficult for the system to give early warnings of dust accumulation risks. For example, when there is an instantaneous strong wind in the construction area or multiple devices are operating simultaneously, the dust diffusion path will change violently, while the existing monitoring systems can often only provide static dust concentration data, unable to predict the dust diffusion trend, nor can they provide targeted dust reduction optimization plans. Therefore, the degree of intelligence and dynamism of dust concentration monitoring in the construction environment still needs to be improved. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a BIM-based safety monitoring method and system for high piers of bridges, which solves the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A BIM-based safety monitoring method for high piers of bridges includes the following steps:

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

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

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

[0011] S4. Execute the dynamic dust reduction strategy of the set D of dust reduction parameters, and obtain the dust concentration error △D after a fixed period;

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

[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), calculate to obtain the sensor layout spacing ds, and deploy a multi-dimensional sensor group in the high pier construction area of the bridge based on the obtained sensor layout spacing ds. The multi-dimensional sensor group includes a dust sensor, a wind speed and direction sensor, an equipment operation status sensor, and an air humidity sensor;

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

[0016] The layout spacing ds of the sensors is obtained through the following calculation formula:

[0017]

[0018] In the formula, 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) in the construction area are collected by the i-th sensor, forming the environmental data collected by the i-th sensor. The LoRa wireless communication transmission method is used for the transmission of the environmental data, and the environmental feature set T composed of the environmental data of all sensors is integrated;

[0020] Among them, the equipment operation status Sm(i) specifically represents the binary value of the start-stop operation status of the equipment collected by the i-th equipment operation status sensor. The binary value specifically represents that 1 means the equipment is in the operation state, and 0 means the equipment is in the shutdown state; 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 the 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 obtained 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 at the position of the i-th sensor in the construction area. By integrating the predicted dust diffusion concentrations Kc of dust PM10 and dust PM2.5 at the positions of all sensors, a dust concentration prediction vector P is formed;

[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 through the following formula:

[0027]

[0028] In the formula, Kc(i, PM10) represents the predicted dust diffusion concentration Kc of PM10 at the i-th sensor area, E(PM10) represents the amount of PM10 dust released from the dust source, which is specifically calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor, U represents the wind speed, which is specifically obtained through the wind speed Ws(i) collected by the i-th sensor, π represents the mathematical constant with a value of 3.14, σy(PM10) and σz(PM10) represent the diffusion coefficients of PM10, specifically representing the diffusion ranges of PM10 dust in the lateral y and vertical z directions, and are obtained through the diffusion empirical formula, y 1 represents the corrected diffusion coordinate in the lateral y direction, specifically representing the corrected diffusion coordinate of the dust diffusing along the wind direction Wd(i) of the actually collected i-th sensor area, represents the diffusion situation of PM10 dust in the lateral y direction, and exp represents the exponential function, represents the diffusion situation of PM10 dust in the vertical z direction, and H(PM10) represents the dust height of the construction equipment releasing PM10 dust, specifically representing the equipment height;

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

[0030]

[0031] In the formula, Kc(i, PM2.5) represents the predicted dust diffusion concentration Kc of PM2.5 at the i-th sensor area, E(PM2.5) represents the amount of PM2.5 dust released from the dust source, which is specifically calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor, U represents the wind speed, which is specifically obtained through the wind speed Ws(i) collected by the i-th sensor, σy(PM2.5) and σz(PM2.5) represent the diffusion coefficients of PM2.5, specifically representing the diffusion ranges of PM2.5 dust in the lateral y and vertical z directions, and are obtained through the diffusion empirical formula, represents the diffusion situation of PM2.5 dust in the lateral y direction, and exp represents the exponential function, represents the diffusion situation of PM2.5 dust in the vertical z direction, and H(PM2.5) represents the dust height of the construction equipment releasing PM2.5 dust, specifically representing the equipment height;

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

[0033]

[0034] In the formula, E(PM10, PM2.5) represents the dust amount E of dust PM10 and dust PM2.5, specifically representing 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, which is specifically measured through 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 state of the j-th equipment, and P(j) represents the power of the j-th equipment;

[0035] The corrected diffusion coordinate y in the transverse y direction 1 is obtained through the following calculation formula:

[0036] y 1 = x * sin(Wd + 180 Ο ) + y * cos(Wd + 180 Ο );

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

[0038] Preferably, S3 includes S31 and S32;

[0039] S31. Conduct dust reduction demand analysis based on the obtained dust concentration prediction vector P. The dust reduction demand analysis obtains whether the i-th sensor area needs dust reduction treatment by comparing the comparison result of the predicted dust diffusion concentration Kc distribution in the construction area with the dust reduction threshold DT, and forms the dust concentration prediction vector P of the area with dust reduction demand by counting the areas that need dust reduction treatment;

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

[0041] The construction area is obtained through the following comparison method:

[0042] When the dust concentration prediction vector P(i, PM10) ≥ DTpm10, or the dust concentration prediction vector P(i, PM2.5) ≥ DTpm2.5, it is obtained that 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, it is obtained that 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 vectors P(i, PM10) and P(i, PM2.5) respectively represent the predicted dust diffusion concentration KcPM10 of dust PM10 and the predicted dust diffusion concentration KcPM2.5 of dust PM2.5 at the i-th sensor area in the dust concentration prediction vector P.

[0045] Preferably, in S32, according to the obtained dust concentration prediction vector P of the area with dust reduction requirements, calculate the dust reduction parameters of the spray system, and obtain the dynamic dust reduction strategy for each area that needs dust reduction treatment. The dynamic dust reduction strategy includes the spray flow rate Q and the dust reduction time T of dust PM10 and dust PM2.5. Among them, after comparing dust PM10 and dust PM2.5 with the dust reduction threshold DT, obtain the maximum value of the dust between dust PM10 and dust PM2.5, and calculate the spray flow rate Q through the maximum value of the dust. By integrating the dynamic dust reduction strategies of each area, form the dust reduction parameter set D;

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

[0047]

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

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

[0050] The dust reduction time T(i, PM10) of dust PM10 is obtained through the following calculation formula:

[0051]

[0052] In the formula, T(i, PM10) represents the dust reduction time of dust PM10 at the i-th sensor area. S(PM10) represents the natural sedimentation rate of dust PM10. Ah(i) represents the air humidity at the i-th sensor area. kh represents the humidity correction coefficient. Ahref represents the reference air humidity;

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

[0054]

[0055] Wherein, T(i, PM2.5) represents the dust settling time of 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. After a fixed period, obtain the dust concentration Dc(i) at the i-th sensor, 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 through the following calculation formula:

[0059] ΔD(i, PM10) = Dc(i, PM10, be) - Dc(i, PM10, af);

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

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

[0062] ΔD(i, PM2.5) = Dc(i, PM2.5, be) - Dc(i, PM2.5, af);

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

[0064] Preferably, S5 includes S51;

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

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

[0067]

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

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

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

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

[0072] The safety monitoring system for high piers of bridges based on BIM includes a BIM model data extraction module, a prediction vector generation module, a dust reduction analysis module, an execution and monitoring module, and a 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 positions, then arranges a plurality of multi-dimensional environmental sensors in the construction area of high piers of bridges, collects the dust diffusion information during the construction process, and forms an environmental feature set T;

[0074] The prediction vector generation module calculates and obtains the dust diffusion concentration based on the obtained environmental feature set T, and forms 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 after 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 for the construction area of high piers of bridges based on the obtained dust concentration error △D, obtains the environmental safety index ZS, compares it with the preset trigger threshold ST for the construction area, and obtains the trigger result for the iterative optimization of the dust reduction parameter set D.

[0078] The present invention provides a method and system for safety monitoring of high piers of bridges based on BIM, having the following beneficial effects:

[0079] (1) By calculating the dust concentration prediction vector P through the environmental feature set T, high-precision prediction of the dust diffusion concentration is achieved, making the dust distribution situation at the construction site more forward-looking. Then, combined with the dynamic changes in the dust concentration, the dust reduction parameter set D is formulated and the dynamic dust reduction strategy is executed, which can adjust the spraying plan according to the real-time environment, effectively improving the dust reduction efficiency and avoiding the problems of water resource waste and uneven coverage 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 triggering threshold ST of the construction area to realize the iterative optimization of the dust reduction parameter set D, ensuring that the construction environment is always within the controllable range and avoiding potential safety hazards caused by excessive dust. Compared with the prior art, this solution can not only improve the accuracy of dust monitoring, but also adaptively adjust the dust reduction plan through the dynamic optimization strategy, greatly improving the intelligent level of air quality management in the construction area and providing a more scientific, efficient and energy-saving dust control plan for the safety of high pier construction of bridges.

[0080] (2) By integrating the data at all sensor positions to form the dust concentration prediction vector P, the simulation of the dust diffusion situation is made more accurate and scientific. Compared with the traditional method relying on single-point monitoring, by comparing the dust concentration prediction vector P with the dust reduction threshold DT, the dust concentration prediction vector P in the area with dust reduction requirements is accurately locked, thus avoiding unnecessary dust reduction operations and improving the response efficiency and accuracy of the spraying system. Based on this, the spraying flow rate Q and the dust reduction time T are further calculated and dynamically adjusted through the maximum predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5, enabling the dust reduction strategy to 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 with different particle sizes, the spraying time is corrected to make the dust reduction plan more intelligent and adaptive. Compared with the traditional construction dust reduction method, this solution can not only accurately predict the dust diffusion situation, but also dynamically adjust the spraying strategy, ensuring the reasonable allocation of dust reduction resources, effectively improving the construction environment quality and guaranteeing construction safety.

[0081] (3) By accurately controlling the spraying equipment at each sensor position through the dust reduction parameter set D and dynamically obtaining the dust concentration error △D of dust PM10 and dust PM2.5 after a fixed period, 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 quantitatively evaluate the dust reduction effect, but also adaptively adjust the spraying flow rate Q and the dust reduction time T based on the actual dust reduction effect, ensuring automatic optimization when the dust reduction effect does not meet the standard and keeping the construction area always within the dust safety threshold range. Brief Description of the Drawings

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

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

[0084] Figure 3 It is the change trend of PM10 and PM2.5 dust concentrations. Detailed Embodiments

[0085] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0086] Embodiment 1

[0087] The present invention provides a safety monitoring method and system for the high pier construction of a bridge based on BIM. 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 positions, then deploy a plurality of multi-dimensional environmental sensors in the high pier construction area of the bridge, collect dust diffusion information during the construction process, and form an environmental feature set T;

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

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

[0091] S4. Execute the dynamic dust reduction strategy of the set D of dust reduction parameters, and obtain a dust concentration error △D after a fixed period;

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

[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, by calculating the dust concentration prediction vector P through the environmental feature set T, the high-precision prediction of the dust diffusion concentration is realized, making the dust distribution situation at the construction site more forward-looking. Then, this solution combines the dynamic changes in the dust concentration, formulates the dust reduction parameter set D, and executes the dynamic dust reduction strategy, which can adjust the spraying plan according to the real-time environment, effectively improving the dust reduction efficiency and avoiding the problems of water resource waste and uneven coverage 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 triggering threshold ST of the construction area to realize the iterative optimization of the dust reduction parameter set D, ensuring that the construction environment is always within the controllable range and avoiding potential safety hazards caused by excessive dust. Compared with the prior art, this solution can not only improve the accuracy of dust monitoring, but also adaptively adjust the dust reduction plan through the dynamic optimization strategy, greatly improving the intelligent level of air quality management in the construction area and providing a more scientific, efficient and energy-saving dust control plan for the safety of high pier bridge construction.

[0094] Embodiment 2

[0095] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: S1 includes S11 and S12;

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

[0097] Among them, the construction equipment operation area includes the operation areas of drilling machines and mixing machines; the dust sensor includes a dust PM10 sensor and a dust PM2.5 sensor;

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

[0099]

[0100] In the formula, 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 through the i-th sensor, forming the environmental data collected by the i-th sensor. The LoRa wireless communication transmission method is used for the transmission of environmental data, and the environmental feature set T composed of the integrated environmental data of all sensors is formed.

[0102] Among them, the equipment operation status Sm(i) specifically represents the binary value of the start-stop operation status of the equipment collected by the i-th equipment operation status sensor. The binary value specifically represents that 1 indicates the equipment is in the operating state, and 0 indicates the equipment is in the shutdown state; 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, by comprehensively calculating the sensor layout spacing ds through the construction area boundary (L, W, H), the accurate layout and optimized distribution of the sensor group are realized, making the construction environment monitoring more systematic and scientific. The multi-dimensional sensor group composed of dust sensors, wind speed and wind direction sensors, equipment operation status sensors, and air humidity sensors can not only comprehensively monitor the dust diffusion situation in the construction area, but also collect the 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 ensures the low power consumption, high stability, and long-distance coverage of the data transmission of the environmental feature set T. Even in a large construction area, the real-time and reliability of data transmission can be guaranteed. It provides 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 the dust diffusion monitoring in the construction area, and provides a more efficient, refined, and intelligent environmental perception scheme for the safety monitoring of high piers of bridges.

[0106] Embodiment 3

[0107] This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 and Figure 3 , specifically: S2 includes S21;

[0108] S21. Based on the obtained environmental feature set T, use the Gaussian diffusion model to calculate the predicted dust diffusion concentrations Kc of dust PM10 and dust PM2.5 at the position of the i-th sensor in the construction area. By integrating the predicted dust diffusion concentrations Kc of dust PM10 and dust PM2.5 at all sensor positions, a dust concentration prediction vector P is formed.

[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 through the following formula:

[0111]

[0112] In the formula, Kc(i, PM10) represents the predicted dust diffusion concentration Kc of PM10 in the i-th sensor area, E(PM10) represents the amount of PM10 dust released from the dust source, which is specifically calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor, U represents the wind speed, which is specifically obtained through the wind speed Ws(i) collected by the i-th sensor, π represents the mathematical constant with a value of 3.14, σy(PM10) and σz(PM10) represent the diffusion coefficients of PM10, specifically representing the diffusion ranges of PM10 dust in the lateral y and vertical z directions, which are calculated and obtained through the diffusion empirical formula, y 1 represents the corrected diffusion coordinate in the lateral y direction, specifically representing the corrected diffusion coordinate of the dust diffusing along the wind direction Wd(i) of the actually collected i-th sensor area. The term 2πUσy(PM10)σz(PM10) is the normalization factor in the formula for predicting the dust diffusion concentration Kc, which is used to ensure that the total amount of the entire pollutant remains consistent and correctly simulate the diffusion process. represents the diffusion situation of PM10 dust in the lateral y direction, exp represents the exponential function, specifically indicating that the farther the lateral y is, the lower the dust concentration. represents the diffusion situation of PM10 dust in the vertical z direction, H(PM10) represents the dust height of the construction equipment releasing PM10 dust, specifically representing the equipment height, specifically indicating that 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 through the following formula:

[0114]

[0115] In the formula, Kc(i, PM2.5) represents the predicted dust diffusion concentration Kc of PM2.5 at the i-th sensor area, E(PM2.5) represents the amount of PM2.5 dust released from the dust source, which is specifically calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor. U represents the wind speed, which is specifically obtained through the wind speed Ws(i) collected by the i-th sensor. σy(PM2.5) and σz(PM2.5) represent the diffusion coefficients of PM2.5, specifically representing the diffusion ranges of PM2.5 dust in the horizontal y and vertical z directions, and are calculated through the diffusion empirical formula. The term 2πUσy(PM2.5)σz(PM2.5) is the normalization factor in the formula for predicting the dust diffusion concentration Kc, which is used to ensure that the total amount of pollutants remains consistent and to correctly simulate the diffusion process. Represents the diffusion situation of PM2.5 dust in the horizontal y direction. exp represents the exponential function, specifically indicating that the farther away in the horizontal y direction, the lower the dust concentration. Represents the diffusion situation of PM2.5 dust in the vertical z direction. H(PM2.5) represents the dust height of the construction equipment releasing PM2.5 dust, specifically representing the equipment height. 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 amount of dust E of dust PM10 and dust PM2.5 is obtained through the following calculation formula:

[0117]

[0118] In the formula, E(PM10, PM2.5) represents the amount of dust E of dust PM10 and dust PM2.5, specifically representing the amount of dust E(PM10) of dust PM10 and the amount of dust E(PM2.5) of dust PM2.5. ek represents the equipment dust release coefficients of dust PM10 and dust PM2.5, which are specifically measured through 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 operation status of the j-th equipment. The operation status Sm(j) of the j-th equipment = 1 indicates that the equipment is in the operating state and releases dust, and the operation status Sm(j) of the j-th equipment = 0 indicates that the equipment is in the shutdown state and does not release dust. P(j) represents the power of the j-th equipment.

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

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

[0121] The corrected diffusion coordinate y in the horizontal y direction 1 is obtained through the following calculation formula:

[0122] y 1 = x * sin(Wd + 180 Ο ) + y * cos(Wd + 180 Ο );

[0123] In the formula, sin represents the sine function, cos represents the cosine function. sin(Wd + 180 Ο ) represents calculating the projection of the dust component in the longitudinal axis x direction on the wind direction Wd, and cos(Wd + 180 Ο ) represents calculating 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. Conduct dust reduction demand analysis based on the obtained dust concentration prediction vector P. The dust reduction demand analysis obtains whether the i-th sensor area needs dust reduction treatment by comparing the predicted dust diffusion concentration Kc distribution in the construction area with the dust reduction threshold DT, and forms the dust concentration prediction vector P of the area with dust reduction demand by counting the areas that need dust reduction treatment.

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

[0127] The construction area is obtained through the following comparison method:

[0128] When the dust concentration prediction vector P(i, PM10) ≥ DTpm10, or the dust concentration prediction vector P(i, PM2.5) ≥ DTpm2.5, it is obtained that 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, it is obtained that 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 vectors P(i, PM10) and P(i, PM2.5) respectively represent the predicted dust diffusion concentration KcPM10 of dust PM10 and the predicted dust diffusion concentration KcPM2.5 of dust PM2.5 at the i-th sensor area in the dust concentration prediction vector P.

[0131] S32. According to the obtained dust concentration prediction vector P of the area with dust reduction requirements, calculate the dust reduction parameters of the spray system, and obtain the dynamic dust reduction strategy for each area that needs dust reduction treatment. The dynamic dust reduction strategy includes the spray flow rate Q and the dust reduction time T of dust PM10 and dust PM2.5. Among them, after comparing dust PM10 and dust PM2.5 with the dust reduction threshold DT, obtain the maximum dust value between dust PM10 and dust PM2.5, and calculate the spray flow rate Q through the maximum dust value. By integrating the dynamic dust reduction strategies of each area, form the dust reduction parameter set D;

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

[0133]

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

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

[0136] The dust reduction time T(i, PM10) of dust PM10 is obtained through the following calculation formula:

[0137]

[0138] Wherein, T(i, PM10) represents the dust settling time of PM10 at the i-th sensor area, S(PM10) represents the natural settling rate of dust PM10, Ah(i) represents the air humidity at the i-th sensor area, kh represents the humidity correction coefficient, which is used to consider the influence of air humidity on the dust settling speed. When the humidity is high, dust particles are easy to adsorb moisture, resulting in an increase in particle size and an acceleration of the settling speed. Therefore, it is necessary to adjust the dust settling time, 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 to standardize the actual air humidity (Ah) and adjust the influence of humidity on the dust settling speed. If the current humidity Ah is high, the settling speed of dust particles is fast, so it is necessary to correct the dust settling time. If the humidity is low and the settling speed slows down, the dust settling time needs to be extended;

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

[0140]

[0141] Wherein, T(i, PM2.5) represents the dust settling time of 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, a Gaussian diffusion model is used to calculate the predicted dust diffusion concentration Kc of dust PM10 and dust PM2.5, and a dust concentration prediction vector P is formed by integrating the data at all sensor positions, making the simulation of the dust diffusion situation 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 state Sm(i) of construction equipment, wind speed Ws(i), wind direction Wd(i), and air humidity Ah(i), ensuring that the spatio-temporal change trend of dust concentration is accurately reflected. In addition, by comparing the dust concentration prediction vector P with the dust suppression threshold DT, this solution accurately locks the dust concentration prediction vector P in the area with dust suppression requirements, thereby avoiding unnecessary dust suppression operations and improving the response efficiency and accuracy of the sprinkler system. Based on this, this solution further calculates the sprinkler flow rate Q and the dust suppression time T, and dynamically adjusts them through the maximum values of the predicted dust diffusion concentrations Kc of dust PM10 and dust PM2.5, enabling the dust suppression strategy to optimize the allocation of water resources according to the real-time environment, ensuring the dust suppression effect while reducing water resource waste. In addition, considering the sedimentation characteristics of dust with different particle sizes, this solution calculates the dust suppression time T(i, PM10) for dust PM10 and the dust suppression time T(i, PM2.5) for dust PM2.5 respectively, and corrects the sprinkler time in combination with the air humidity Ah(i), making the dust suppression plan more intelligent and adaptive. Compared with the traditional construction dust suppression method, this solution can not only accurately predict the dust diffusion situation, but also dynamically adjust the sprinkler strategy, ensuring the reasonable allocation of dust suppression resources, ultimately improving the accuracy, response speed and dust suppression efficiency of dust management in the construction area, effectively improving the construction environment quality and ensuring construction safety.

[0143] Example 4

[0144] This embodiment is an explanatory description carried out in Example 3. Please refer to Figure 1 , specifically: S4 includes S41;

[0145] S41. Extract each sensor position in the dust suppression parameter set D according to the obtained dust suppression parameter set D, and execute the dynamic dust suppression strategy for each sensor position by controlling the sprinkler equipment. After a fixed period, obtain the dust concentration Dc(i) at the i-th sensor, and then compare it with the dust concentration Dc(i) at the i-th sensor obtained before executing the dynamic dust suppression 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 through the following calculation formula:

[0147] ΔD(i,PM10)=Dc(i,PM10,be)-Dc(i,PM10,af);

[0148] In the formula, the dust concentration error ΔD(i, PM10) represents the dust concentration error of PM10 at the i-th sensor, Dc(i, PM10, be) represents the dust concentration of PM10 before implementing the dynamic dust reduction strategy at the i-th sensor, and Dc(i, PM10, af) represents the dust concentration of PM10 after implementing the dynamic dust reduction strategy at the i-th sensor;

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

[0150] ΔD(i, PM2.5) = Dc(i, PM2.5, be) - Dc(i, PM2.5, af);

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

[0152] S5 includes S51;

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

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

[0155]

[0156] In the formula, wpm10 and wpm2.5 respectively represent the preset weight values of PM10 and PM2.5, and the specific values are set by empirical values, and N represents the preset number of sensors.

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

[0158] The number of sensors N = 3;

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

[0160] The dust concentration error ΔD(i, PM10) of PM10 at the i-th sensor: 20, 25, 22;

[0161] The maximum predicted dust diffusion concentration Kc of PM2.5 in the dust concentration prediction vector P: 250;

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

[0163] The preset weight value of PM10: 0.5;

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

[0165] The construction area trigger threshold ST: 0.85;

[0166] Calculate the average error of PM2.5:

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

[0168] And perform normalization: 35 / 250 = 0.14;

[0169] Calculate the average error of PM10:

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

[0171] And perform normalization: 22.33 / 150 = 0.149;

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

[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 for the iterative optimization of the dust reduction parameter set D is the non-trigger result;

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

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

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

[0179] In this embodiment, the spraying equipment at each sensor position is accurately controlled through the dust reduction parameter set D, and the dust concentration errors ΔD of dust PM10 and dust PM2.5 are 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 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 the dust PM2.5 dust concentration Dc(i, PM2.5, be) at the i-th sensor before and after implementing the dynamic dust reduction strategy in real time, so as to accurately calculate the dust concentration error ΔD(i, PM10) of dust PM10 and the dust concentration error ΔD(i, PM2.5) of dust PM2.5, ensuring that the reduction of dust concentration 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, enabling the dust reduction system to 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, making the calculation of the environmental safety index ZS more in line with the actual air quality evaluation standard of the construction site. Compared with the fixed dust reduction strategy, this solution can not only quantitatively evaluate the dust reduction effect, but also adaptively adjust the spraying flow rate Q and the dust reduction time T based on the actual dust reduction effect, ensuring automatic optimization when the dust reduction effect does not meet the standard, and keeping the construction area within the dust safety threshold range at all times. Finally, through the feedback control and iterative optimization mechanism, this solution constructs an efficient, accurate, and continuously optimizable intelligent monitoring and dust reduction management system for the dust in the bridge pier construction area, greatly improving the air quality of the construction environment, ensuring the health and safety of construction workers, and at the same time optimizing the water resource utilization efficiency.

[0180] Example 5

[0181] For the BIM-based bridge pier construction safety monitoring method and system, please refer to Figure 2 , specifically: including a BIM model data extraction module, a prediction vector generation module, a dust reduction analysis module, an execution and monitoring module, and a 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 positions, then arranges multiple multi-dimensional environmental sensors in the bridge pier construction area, collects the dust diffusion information during the construction process, and forms the environmental feature set T;

[0183] The prediction vector generation module calculates and obtains the dust diffusion concentration based on the obtained environmental feature set T, and forms the dust concentration prediction vector P;

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

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

[0187] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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; 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 does not require dust reduction treatment; 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; 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 at the i-th sensor area, S(PM10) represents the natural settling rate of dust PM10, Ah(i) represents the air humidity at 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; 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) 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; The equipment operation state Sm(i) specifically represents the binary value of the equipment start-stop operation state collected by the i-th equipment operation state sensor, and the binary value is specifically represented as 1 for the equipment being in operation and 0 for the equipment being in shutdown state; at the same time, when the equipment operation state Sm(i)=1, the load intensity of the equipment is recorded as the associated data of the equipment operation state 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 predicted PM10 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, π represents a mathematical constant with a value of 3.14, σy(PM10) and σz(PM10) represent the diffusion coefficients of PM10, which specifically represent the diffusion range of PM10 dust in the lateral y and vertical z directions, which are calculated and obtained by the diffusion empirical formula, y 1 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 predicted PM2.5 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 specifically calculated and obtained through the equipment operation status Sm(i) collected by the i-th sensor, U represents the wind speed, which is specifically obtained through the wind speed Ws(i) collected by the i-th sensor, σy(PM2.5) and σz(PM2.5) represent the diffusion coefficient of PM2.5, which specifically represents the diffusion range of PM2.5 dust in the horizontal y direction and the vertical z direction, which is calculated and obtained through 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 y in the lateral y direction 1 Obtained through the following calculation formula: and 1 =x*sin(Wd+180°)+y*cos(Wd+180°); 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 1 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 at the i-th sensor after a fixed period, and then compare it with the dust concentration at the i-th sensor obtained before executing the dynamic dust reduction strategy to obtain the dust concentration error △D of PM10 and PM2.5; The dust concentration error △D(PM10) of dust PM10 is obtained by the following calculation formula: ΔD(i,PM10)=Dc(i,PM10,be)-Dc(i,PM10,af); 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: ΔD(i,PM2.5)=Dc(i,PM2.5,be)-Dc(i,PM2.5,af); 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.

6. The BIM-based bridge high pier construction safety monitoring method according to claim 5 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.

7. A BIM-based bridge high pier construction safety monitoring system is applied to a BIM-based bridge high pier construction safety monitoring method according to any one of claims 1 to 6, 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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