Building engineering management system and management method

Abstract

The invention discloses a building engineering management system and management method, and relates to the technical field of construction dust risk management. Dust monitoring points are arranged in a construction site, dust related parameters of a plurality of dust monitoring areas are monitored in real time, a diffusion data set and a mechanical operation distribution data set are combined, and construction dust risk management is performed. And carrying out deep calculation on the data by utilizing a neural network technology, and accurately obtaining the ground dust raising intensity, the mechanical operation dust raising index and the material storage dust raising diffusion ratio of each dust raising monitoring area. And performing association to obtain a regional dust raising comprehensive index Yczsi of the ith dust raising monitoring region, and then performing evaluation to automatically generate a risk grading result. According to the intelligent evaluation based on the data, errors caused by manual estimation are avoided. And according to the grading result, the system can automatically adjust dust raising control measures, such as watering, dust screen setting or construction plan adjustment, so that reasonable control measures are adopted in different risk grade areas, and the generation of dust raising pollution is further reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of construction dust risk management, and in particular to a construction engineering management system and a management method. Background Art

[0002] As the scale of construction projects continues to expand, the dust problem generated during the construction process has become more and more serious, especially in urban construction, high-rise buildings and other projects. Dust pollution has become an important issue affecting construction site safety, worker health and the quality of the surrounding environment. Construction dust not only poses a potential threat to the health of workers on the construction site, but may also have a negative impact on the quality of life of surrounding residents. In serious cases, it may even cause traffic accidents or affect the safety of other infrastructure. In addition, the dust problem may also cause penalties from environmental regulatory authorities, affecting the corporate image and construction progress. Therefore, how to effectively control dust on construction sites and ensure the safety and environmental protection of the construction environment has become an important issue that needs to be urgently solved in the construction industry.

[0003] In traditional construction project management, traditional dust control methods are often unable to accurately identify dust sources and diffusion areas, resulting in waste of resources and ineffective control. Manual judgment and execution methods lack systematicity and data support, and are prone to over-control or under-control. Traditional dust monitoring methods often rely on manual inspections and regular checks, which are unable to track dust changes during construction in real time, and it is difficult to take timely measures when dust concentrations exceed the standard, thus failing to effectively curb the spread of dust pollution. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a construction project management system and a management method to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a construction project management system, including a regional division module, a dust monitoring module, a dust assessment module and a smart management module;

[0006] The area division module is used to pre-select a construction site for a building project and divide the construction site into a number of dust monitoring areas, while setting monitoring points in the dust monitoring areas;

[0007] The dust monitoring module is used to monitor and record the dust-related parameter information generated during the construction of the building project in several dust monitoring areas, and to construct a diffusion data set and a mechanical operation distribution data set;

[0008] The dust source distribution model is constructed and trained using neural network technology, and the diffusion data set and mechanical operation distribution data set are deeply calculated to obtain the ground dust intensity Gds in the i-th dust monitoring area. i, mechanical operation dust emission index Jyz i and material storage dust diffusion ratio Mfb i ;

[0009] The dust emission assessment module is used to correlate the ground dust emission intensity Gds i , mechanical operation dust emission index Jyz i and material storage dust diffusion ratio Mfb i in the i-th dust emission monitoring area to construct the regional dust emission comprehensive index Yczs i in the i-th dust emission monitoring area; and a dust emission risk threshold Cz is preset to generate a comparison grading result of the regional dust emission comprehensive index Yczs i in the i-th dust emission monitoring area and the dust emission risk threshold Cz;

[0010] The intelligent management module is used to dynamically adjust the dust control measures in the corresponding area according to the comparison grading result, including automatic sprinkling, setting up dust-proof nets or optimizing the construction plan.

[0011] Preferably, the area division module includes a construction site data collection unit and a monitoring area division unit;

[0012] The construction site data collection unit is used to collect the geometric shape, topography, elevation data of the construction site, as well as the distribution information of construction equipment and materials by using unmanned aerial vehicle aerial photography, three-dimensional laser scanning or geographic information system GIS technology, and form a dust emission diffusion potential model of the construction site;

[0013] The monitoring area division unit is used to equally divide the construction site into several dust emission monitoring areas and visually mark them as Q1, Q2, Q3,..., Q n in the dust emission diffusion potential model of the construction site, where n represents the total number of dust emission monitoring areas.

[0014] Preferably, the dust emission monitoring module includes a first monitoring unit and a first calculation unit;

[0015] The first monitoring unit is used to monitor and record the material storage distribution and mechanical operation distribution information of several dust emission monitoring areas, and establish a diffusion data set and a mechanical operation distribution data set;

[0016] The expression of the diffusion data set is to are the management data of sand, cement and coal ash materials in the first to the n-th dust emission monitoring areas respectively, and t represents the proportion of the exposed area of sand, cement and coal ash materials;

[0017] The management data of sand, cement, and coal ash materials in the dust monitoring area includes material name, material proportion, and material exposed area;

[0018] The expression of the mechanical operation distribution data set is to are the dust construction operation distribution data of the first to the nth dust monitoring areas respectively. The dust construction operation distribution data includes the disturbed soil data during the operation of excavators and bulldozers, the dust distribution data generated by demolishing walls or cement blocks, and the dust distribution data during drilling or cutting operations; s represents the proportion of dust diffusion caused by dust construction operations.

[0019] Preferably, the first calculation unit is used to perform denoising, layering, and storage processing on the diffusion data set and the mechanical operation distribution data set through big data analysis technology, and use the neural network model CNN technology to construct a dust source distribution model;

[0020] After training the dust source distribution model, perform in-depth analysis and calculation on the diffusion data set and the mechanical operation distribution data set to obtain: the ground dust intensity Gds of the ith dust monitoring area i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i ;

[0021] The acquisition method of the ground dust intensity Gds of the ith dust monitoring area i includes the following steps:

[0022] S11. Collect the real-time wind speed V w through an anemometer, and calculate the wind speed correction coefficient W using the following formula:

[0023] W = (V w / V c ) u ;

[0024] In the formula, V c is the critical wind speed threshold, and u is the particle sensitivity coefficient, set as u = 2;

[0025] S12. Through ground remote sensing monitoring equipment, including drones or satellite images, collect and calculate the exposed ground area A of the ith dust monitoring area in real time, and obtain the soil coverage rate C f through a soil coverage monitoring sensor or image processing technology, and represent the area proportion of the soil coverage rate in a proportional form, with a range of 0 - 1;

[0026] S13. Collect soil samples in the ith dust monitoring area through a soil sampling device, and measure the dry density ρ of the soil using a soil drying tester;

[0027] S14. The wind speed correction coefficient W, the bare ground area A, and the soil coverage rate C collected or calculated in S11 - S13 f After dimensionless processing, calculate the ground dust emission intensity Gds of the i-th dust monitoring area through the following formula i :

[0028] Gds i = A * ρ * (1 - C f ) * W * V;

[0029] In the formula, A represents the bare ground area, ρ represents the dry density of the soil, C f represents the soil coverage rate, W represents the wind speed correction coefficient, and V represents the instantaneous wind speed measured at the monitoring point.

[0030] Preferably, the method for obtaining the mechanical operation dust index Jyz of the i-th dust monitoring area i includes the following steps:

[0031] S21. Through the dust monitoring equipment or construction log, count the real-time mechanical operation frequency N m in the i-th dust monitoring area, with the unit of kg / event, and record the construction duration T;

[0032] S22. According to the construction machinery type and operation intensity, look up or calculate the dust emission amount E of a single mechanical operation through the dust emission coefficient library m , including the dust emission amounts of different types of construction machinery, and the construction machinery includes excavators, bulldozers, and rollers, which are determined by experimental data or standard parameters;

[0033] S23. By collecting the weight of each construction machinery and collecting the air humidity, calculate and obtain the dust diffusion correction factor F through the following formula d :

[0034]

[0035] In the formula, k represents the initial proportionality coefficient of the correction factor, which depends on experimental calibration, and the typical value is 1, Z i represents the total weight of the construction machinery in the i-th construction monitoring area, Z ref represents the maximum mechanical weight threshold, H α represents the air humidity, m represents the mechanical weight sensitivity coefficient, m = 1.5; j represents the air humidity sensitivity coefficient, j = 2;

[0036] S24. Extract the proportion s of dust diffusion caused by dust construction operations in the mechanical operation distribution dataset;

[0037] S25. Extract the mechanical real-time operation frequency N in S21 - S25 m , the construction duration T, and the dust emission amount E of a single mechanical operation m , the dust diffusion ratio s caused by the dust-generating construction operation and the dust diffusion correction factor F d . After dimensionless processing, the mechanical operation dust index Jyz of the i-th dust monitoring area is calculated through the following formula i :

[0038]

[0039] Preferably, the material storage dust diffusion ratio Mfb of the i-th dust monitoring area i is obtained through the following steps:

[0040] S31. Detect the dust storage amount in the material storage area of the i-th dust monitoring area through a dust detection device to obtain the dust storage amount M d , and extract the proportion t of the exposed sand, cement, and coal ash material area in the i-th dust monitoring area in the diffusion dataset;

[0041] S32. Measure the average diameter D of the dust particles in the material storage area of the i-th dust monitoring area through a particle sensor p , with the unit of μm, and take the average value based on multiple measurements. The expression is as follows:

[0042]

[0043] where D r represents the dust particle diameter of the r-th measurement, and g represents the total number of measurements;

[0044] S33. Measure the boundary height H of the material storage area of the i-th dust monitoring area through an altimeter, specifically the enclosure height;

[0045] S34. Extract the dust storage amount M d , the proportion t of the exposed sand, cement, and coal ash material area, the average diameter D of the dust particles p and the boundary height H of the material storage area. After dimensionless processing, the material storage dust diffusion ratio Mfb of the i-th dust monitoring area is calculated through the following formula i :

[0046]

[0047] In the formula, when the dust storage amount M d and the proportion t of the exposed material area increase, Mfb i increases, reflecting the improvement of the potential for dust diffusion. The average diameter D of the dust particlesp And the greater the boundary height H of the material storage area, the smaller Mfb i is, indicating that higher enclosures or larger particles help reduce dust diffusion.

[0048] Preferably, the dust evaluation module includes a first fitting unit and a first evaluation unit;

[0049] The first fitting unit is used to correlate the ground dust intensity Gds i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i in the i-th dust monitoring area to construct the comprehensive dust index Yczs i of the i-th dust monitoring area:

[0050] Yczs i = Gds i * w1 + Jyz i * w2 + Mfb i * w3;

[0051] In the formula, w1, w2, and w3 respectively represent the weights of the ground dust intensity Gds i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i in the i-th dust monitoring area, and w1 + w2 + w3 = 1;

[0052] The first evaluation unit is used to preset the dust risk threshold Cz and compare and evaluate the comprehensive dust index Yczs i of the i-th dust monitoring area with the dust risk threshold Cz to obtain a classification result, including:

[0053] When the comprehensive dust index Yczs i of the i-th dust monitoring area > the dust risk threshold Cz * 150%, it indicates that there is a dust risk in this dust monitoring area and it is the first severe risk level;

[0054] When the dust risk threshold Cz * 120% ≤ the comprehensive dust index Yczs i of the i-th dust monitoring area ≤ the dust risk threshold Cz * 150%, it indicates that there is a dust risk in this dust monitoring area and it is the second severe risk level, which is lower than the first severe risk level;

[0055] When the dust risk threshold Cz ≤ the comprehensive dust index Yczs i≤120% of the dust emission risk threshold Cz, indicating that there is a dust emission risk in this dust monitoring area, and it is the third most severe risk level, which is lower than the second most severe risk level;

[0056] When the comprehensive dust index Yczs of the i-th dust monitoring area i <the dust emission risk threshold Cz, indicating that there is no dust emission risk in this dust monitoring area, and continuous monitoring is carried out.

[0057] Preferably, the intelligent management module includes a first strategy unit and a strategy correction unit;

[0058] The first strategy unit is used to generate corresponding control strategies according to the classification results generated by the first evaluation unit, including:

[0059] Generating the first control strategy according to the first severe risk level includes: setting 3 additional sprinkler trucks or high-pressure spray devices in this lift monitoring area, and controlling the sprinkling frequency distribution at different time periods, including:

[0060] Morning: 6:00 - 9:00, 2 times per hour;

[0061] Noon: 12:00 - 14:00, 3 times per hour;

[0062] Evening: 17:00 - 20:00, 2 times per hour;

[0063] Increasing the boundary height H of the current 15% - 30% of the material storage area in this lift monitoring area, and setting dust-proof nets to cover the materials in the material storage area to ensure a dust-proof net coverage area of 50%; reducing the current real-time operation frequency of machinery by 15%;

[0064] Generating the second control strategy according to the second severe risk level includes: setting 2 additional sprinkler trucks or high-pressure spray devices in this lift monitoring area, and controlling the sprinkling frequency distribution at different time periods, including:

[0065] Morning: 6:00 - 9:00, 1 time per hour;

[0066] Noon: 12:00 - 14:00, 2 times per hour;

[0067] Evening: 17:00 - 20:00, 1 time per hour;

[0068] Increasing the boundary height H of the current 8% - 14% of the material storage area in this lift monitoring area, and setting dust-proof nets to cover the materials in the material storage area to ensure a dust-proof net coverage area of 30 - 49%; reducing the current real-time operation frequency of machinery by 10%;

[0069] The third control strategy generated according to the third serious risk level includes: setting up an additional sprinkler truck or high-pressure spray device in the head monitoring area, and controlling the distribution of sprinkler frequency in different time periods, including:

[0070] Morning: 6:00-9:00, once every 1.5 hours;

[0071] Lunch: 12:00-14:00, 2 times every 1.5 hours;

[0072] Evening: 17:00-20:00, once every 1.5 hours;

[0073] Increase the current 3%-7% material storage area boundary height H in the lift monitoring area, and set a dustproof net to cover the materials in the material storage area to ensure that the dustproof net coverage area reaches 20-29%; reduce the current mechanical real-time operation frequency by 5%.

[0074] Preferably, the strategy correction unit is used to recalculate and obtain the regional dust comprehensive index Yczs of the i-th dust monitoring area after executing the first control strategy, the second control strategy and the third control strategy. i Then, the steps of the dust monitoring module and the dust assessment module are repeated. If the classification result of the first assessment unit changes, the strategy correction unit automatically upgrades or downgrades the control strategy.

[0075] A construction project management method comprises the following steps:

[0076] Step 1: Pre-select a construction site for a building project and divide the construction site into several dust monitoring areas, and set up monitoring points in the dust monitoring areas;

[0077] Step 2: Monitor and record the dust-related parameter information generated during the construction of several dust monitoring areas, and construct a diffusion data set and a mechanical operation distribution data set;

[0078] The dust source distribution model is constructed and trained using neural network technology, and the diffusion data set and mechanical operation distribution data set are deeply calculated to obtain the ground dust intensity Gds in the i-th dust monitoring area. i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i ;

[0079] Step 3: Used to calculate the ground dust intensity Gds in the i-th dust monitoring area i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i Correlate to construct the regional dust comprehensive index Yczs of the i-th dust monitoring areai ; and a preset dust emission risk threshold Cz is used to generate the regional dust emission comprehensive index Yczs of the i-th dust emission monitoring area i and the comparison grading result of the dust emission risk threshold Cz;

[0080] Step Four: Dynamically adjust the dust emission control measures in the corresponding area according to the comparison grading result, including automatic sprinkling, setting up dust-proof nets or optimizing the construction plan.

[0081] The present invention provides a construction project management system and a management method. It has the following beneficial effects:

[0082] (1) For the construction project management system and management method, through the collaborative work of the area division module and the dust emission monitoring module, the construction site can be accurately divided into multiple dust emission monitoring areas, and monitoring points are set in each area to track the specific sources of dust emission and their diffusion areas in real time. This makes the monitoring of dust emission more detailed and effectively avoids the problem of recognition lag caused by manual inspection and regular inspection in the traditional method.

[0083] (2) For the construction project management system and management method, the dust emission monitoring module constructs a diffusion data set and a mechanical operation distribution data set, and uses neural network technology to construct a dust emission source distribution model for in-depth calculation, and can scientifically evaluate the ground dust emission intensity, mechanical operation dust emission index and material storage dust emission diffusion ratio of each dust emission monitoring area. This intelligent analysis promotes the improvement of the evaluation accuracy of dust pollution and can adjust the control strategy in real time according to different construction conditions.

[0084] (3) For the construction project management system and management method, the dust emission evaluation module calculates the associated dust emission indicators, specifically the regional dust emission comprehensive index Yczs of the i-th dust emission monitoring area i , and compares it with the preset dust emission risk threshold. According to the grading result, the intelligent management module can automatically adjust the dust emission control measures, such as automatic sprinkling, setting up dust-proof nets or optimizing the construction plan. This intelligent dynamic adjustment not only improves the response speed of dust emission control, but also effectively avoids the situation of resource waste or over-control.

[0085] (4) For the construction project management system and management method, through precise dust emission control and real-time response, the system can effectively reduce the impact of dust emission on the health of construction workers, the surrounding environment and traffic safety. At the same time, it avoids environmental supervision penalties caused by dust emission problems, helps to enhance the social responsibility and industry reputation of the enterprise. Compared with the traditional manual inspection and regular inspection methods, the intelligent system of the present invention can reduce manual intervention, lower management and implementation costs. Through automated dust emission monitoring and control, it promotes the improvement of management efficiency and reduces unnecessary resource waste. Description of the Drawings

[0086] Figure 1 This is a schematic diagram of the process of a building engineering management system according to the present invention;

[0087] Figure 2 This is a schematic diagram of the steps of a building engineering management method according to the present invention. Specific embodiments

[0088] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0089] Embodiment 1

[0090] Please refer to Figure 1 , the present invention provides a building engineering management system, including a regional division module, a dust monitoring module, a dust assessment module, and an intelligent management module;

[0091] The regional division module is used to pre-select the construction site of the building project and divide the construction site into several dust monitoring areas, and at the same time set monitoring points within the dust monitoring areas;

[0092] The dust monitoring module is used to monitor and record the dust-related parameter information generated during the construction of the building project in several dust monitoring areas, and construct a diffusion data set and a mechanical operation distribution data set;

[0093] And after using neural network technology to construct and train a dust source distribution model, perform in-depth calculations on the diffusion data set and the mechanical operation distribution data set to obtain the ground dust intensity Gds of the i-th dust monitoring area i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i ;

[0094] The dust assessment module is used to correlate the ground dust intensity Gds of the i-th dust monitoring area i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i to construct the regional dust comprehensive index Yczs of the i-th dust monitoring area i ; and preset a dust risk threshold Cz to generate a comparison grading result between the regional dust comprehensive index Yczs of the i-th dust monitoring area i and the dust risk threshold Cz;

[0095] The intelligent management module is used to dynamically adjust the dust control measures in the corresponding area according to the comparison and grading results, including automatic sprinkling, setting up dust-proof nets or optimizing the construction plan.

[0096] In this embodiment, through the collaborative work of the area division module and the dust monitoring module, the construction site can be accurately divided into multiple dust monitoring areas, and monitoring points are set in each area to track the specific sources of dust generation and their diffusion areas in real time. This makes the monitoring of dust more meticulous and effectively avoids the problem of recognition lag caused by manual inspections and regular checks in traditional methods.

[0097] The dust monitoring module can scientifically evaluate the ground dust intensity, mechanical operation dust index, and material storage dust diffusion ratio in each dust monitoring area by constructing a diffusion data set and a mechanical operation distribution data set, and using neural network technology to construct a dust source distribution model for in-depth calculation. This intelligent analysis greatly improves the evaluation accuracy of dust pollution and can adjust the control strategy in real time according to different construction situations.

[0098] The dust evaluation module calculates the associated dust index, specifically the comprehensive dust index Yczs of the i-th dust monitoring area i , and compares it with the preset dust risk threshold. According to the grading result, the intelligent management module can automatically adjust the dust control measures, such as automatic sprinkling, setting up dust-proof nets or optimizing the construction plan. This intelligent dynamic adjustment not only improves the response speed of dust control but also effectively avoids situations of resource waste or over-control.

[0099] Through precise dust control and real-time response, the system can effectively reduce the impact of dust on the health of construction workers, the surrounding environment, and traffic safety. At the same time, it avoids environmental supervision penalties caused by dust problems, which helps to enhance the social responsibility and industry reputation of the enterprise. Compared with the traditional manual inspection and regular inspection methods, the intelligent system of the present invention can reduce manual intervention, lower management and implementation costs. Through automated dust monitoring and control, it promotes the improvement of management efficiency and reduces unnecessary resource waste.

[0100] Embodiment 2

[0101] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the area division module includes a construction site data collection unit and a monitoring area division unit;

[0102] The construction site data collection unit is used to collect the geometric shape, topography, elevation data, and the distribution information of construction equipment and materials of the construction site by using unmanned aerial vehicle aerial photography, three-dimensional laser scanning, or geographic information system (GIS) technology to form a construction site dust diffusion potential model;

[0103] The monitoring area division unit is used to equally divide the construction site into several dust monitoring areas, and visually mark them in the construction site dust diffusion potential model as Q1, Q2, Q3, ..., Q n , where n represents the total number of dust monitoring areas.

[0104] In this embodiment, the construction site data acquisition unit can accurately obtain the geometric shape, topography, elevation data, and the distribution information of construction equipment and materials of the construction site by using technologies such as UAV aerial photography, 3D laser scanning, and geographic information system (GIS). These high-precision data provide a solid foundation for the subsequent construction of the dust diffusion potential model, ensuring the accurate assessment of the dust diffusion potential of the construction site. The data obtained by the construction site data acquisition unit can form a detailed dust diffusion potential model. This model not only takes into account physical factors such as terrain, topography, and elevation, but also combines the distribution information of equipment and materials at the construction site to comprehensively evaluate the possibility and intensity of dust diffusion, providing a scientific basis for subsequent dust monitoring and control. The monitoring area division unit divides the construction site into multiple equally spaced dust monitoring areas according to the dust diffusion potential model of the construction site. Through this reasonable division, it can ensure the accurate monitoring of the dust intensity in each monitoring area, avoiding the problem of monitoring blind spots caused by unreasonable monitoring point settings in traditional methods. In addition, the visual marking method of the construction site dust diffusion potential model makes the area division more intuitive and easy to understand, facilitating managers to quickly identify and adjust the dust monitoring strategy.

[0105] Embodiment 3

[0106] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the dust monitoring module includes a first monitoring unit and a first calculation unit;

[0107] The first monitoring unit is used to monitor and record the material storage distribution and mechanical operation distribution information of several dust monitoring areas, and establish a diffusion data set and a mechanical operation distribution data set;

[0108] The expression of the diffusion data set is to are the management data of sand, cement, and coal ash materials in the first to the nth dust monitoring areas respectively, and t represents the proportion of the exposed area of sand, cement, and coal ash materials; the management data of sand, cement, and coal ash materials in the dust monitoring area includes the material name, material proportion, and material exposed area;

[0109] The expression of the mechanical operation distribution data set is to Dust construction operation distribution data for the first to the nth dust monitoring areas respectively. The dust construction operation distribution data includes data on soil disturbance during the operation of excavators and bulldozers, dust distribution data generated from demolishing walls or cement blocks, and dust distribution data during drilling or cutting operations; s represents the proportion of dust diffusion caused by dust construction operations.

[0110] In this embodiment, the acquired monitoring data is integrated into a diffusion dataset, which includes a diffusion dataset and a mechanical operation distribution dataset. The construction of such a dataset not only considers the proportion of material storage and the exposed area, but also includes data on soil disturbance and dust distribution during the operation of construction equipment. Through the integration of multi-dimensional data, it provides rich analysis basis for the accurate identification of dust sources, the assessment of dust intensity, and the prediction of diffusion paths.

[0111] Based on the data of dust construction operations and material storage, the dust diffusion intensity and risk of each monitoring area can be calculated more precisely. For example, by recording the dust generation situation during earthwork operations (such as excavators, bulldozers, etc.), the proportion of dust diffusion (s) in this area can be calculated in real time. Such precise dust source analysis enables targeted adjustment of dust control measures. For example, in areas with higher dust risks, more effective control measures such as sprinkling water and covering with dust-proof nets can be implemented. By establishing a diffusion dataset and a mechanical operation distribution dataset, not only can the changes of dust sources be monitored in real time, but also the dust situation can be dynamically adjusted at different construction stages. For example, when certain construction activities or material storage cause higher dust risks, the system can automatically issue warnings and take control measures to reduce the impact of dust on the environment. This intelligent and dynamic dust management method greatly improves the efficiency and timeliness of dust control.

[0112] Embodiment 4

[0113] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the first calculation unit is used to perform denoising, layering, and storage processing on the diffusion dataset and the mechanical operation distribution dataset through big data analysis technology, and use the neural network model CNN technology to construct a dust source distribution model;

[0114] After training the dust source distribution model, perform in-depth analysis and calculation on the diffusion dataset and the mechanical operation distribution dataset to obtain: the ground dust intensity Gds of the ith dust monitoring area i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i ;

[0115] The ground dust intensity Gds of the ith dust monitoring area iThe acquisition method includes the following steps:

[0116] S11. Collect the real-time wind speed V through an anemometer w , and calculate the wind speed correction coefficient W using the following formula:

[0117] W = (V w / V c ) u ;

[0118] In the formula, V c is the critical wind speed threshold, u is the particle sensitivity coefficient, which is set to u = 2; by collecting the real-time wind speed and using the wind speed correction coefficient W to correct the dust emission intensity, it can more accurately reflect the impact of different wind speeds on dust diffusion. Wind speed is a key factor affecting dust diffusion, and its change will directly affect the diffusion distance and intensity of dust. By introducing the wind speed correction coefficient, the dust emission intensity assessment can be dynamically adjusted according to different wind speed conditions, making dust prediction and management more accurate and reliable.

[0119] S12. Through ground remote sensing monitoring equipment, including drones or satellite images, collect and calculate the bare ground area A of the i-th dust emission monitoring area in real time, and obtain the soil coverage rate C through soil coverage monitoring sensors or image processing technology f , and express the area proportion of the soil coverage rate in proportion, with a range of 0-1; comprehensively understand the ground conditions of the dust emission source. Bare soil and areas with insufficient coverage are prone to dust emission. Therefore, accurately measuring the bare ground and soil coverage rate provides a key quantitative basis for dust prevention and control. Through this process, dust emission sources can be more effectively identified, and prevention and control measures can be taken preferentially to reduce the impact of dust on the environment and construction workers.

[0120] S13. Collect soil samples in the i-th dust emission monitoring area through soil sampling equipment, and measure the soil dry density ρ using a soil drying tester; by measuring the soil dry density through soil sampling equipment, the dust emission potential under different soil conditions can be further confirmed, providing more accurate soil characteristic data for the calculation of dust emission intensity. Combining the soil dry density can more accurately evaluate the actual occurrence of dust emission, thus providing a more targeted decision-making basis for dust control strategies.

[0121] S14. After dimensionless processing of the wind speed correction coefficient W, bare ground area A, and soil coverage rate C collected or calculated in S11 - S13 f , calculate the ground dust emission intensity Gds of the i-th dust emission monitoring area through the following formula i :

[0122] Gds i = A * ρ * (1 - C f ) * W * V;

[0123] In the formula, A represents the bare ground area, ρ represents the dry density of the soil, C f represents the soil coverage rate, W represents the wind speed correction factor, and V represents the instantaneous wind speed measured at the monitoring point. By combining the wind speed correction factor W, the bare ground area A, the dry density of the soil, and the soil coverage rate to calculate the ground dust emission intensity, it is possible to more comprehensively and accurately evaluate the dust emission level of each dust monitoring area. This comprehensive calculation method not only considers the influence of wind speed, but also combines the physical properties of the soil and the soil coverage situation, making the evaluation of dust emission intensity more scientific and dynamic. This method effectively improves the accuracy of dust management and provides refined data support for dust control.

[0124] The mechanical operation dust index Jyz of the i-th dust monitoring area i is obtained through the following steps:

[0125] S21. Through the dust monitoring equipment or construction logs, count the real-time operation frequency N of the machinery in the i-th dust monitoring area m , with the unit of kg / event, and record the construction duration T; by counting the real-time operation frequency N of the construction machinery m and the construction duration T, it is possible to quantitatively understand the frequency and duration of mechanical operations, providing accurate input data for subsequent calculation of dust emissions. These data directly affect the evaluation of dust emissions and help formulate more targeted dust prevention and control measures.

[0126] S22. According to the type and operation intensity of the construction machinery, look up or calculate the dust emission amount E of a single mechanical operation through the dust emission coefficient library m , including the dust emission amounts of different types of construction machinery. The construction machinery includes excavators, bulldozers, and rollers, which are determined by experimental data or standard parameters; according to the type and operation intensity of the construction machinery, look up or calculate the dust emission amount of a single mechanical operation through the dust emission coefficient library, making the evaluation of dust more accurate. The emission coefficients of different types of machinery can be determined according to experimental data or standard parameters, so as to accurately estimate the dust emission amount according to the type and intensity of mechanical operations. This helps to identify the key construction links with high dust emissions, optimize the construction plan, and reduce the impact of dust on the environment.

[0127] S23. By collecting the weight of each construction machinery and collecting the air humidity, calculate and obtain the dust diffusion correction factor F through the following formula d :

[0128]

[0129] where k represents the initial proportionality coefficient of the correction factor, which depends on experimental calibration and typically has a value of 1, Z i represents the total weight of construction machinery in the i-th construction monitoring area, Z ref represents the maximum mechanical weight threshold, H α represents the air humidity, m represents the mechanical weight sensitivity coefficient, m = 1.5; j represents the air humidity sensitivity coefficient, j = 2; by collecting the weight and air humidity of construction machinery, the dust diffusion correction factor can be calculated to dynamically adjust the influencing factors of dust emission, considering the impact of changes in mechanical weight and air humidity on dust diffusion. This step makes the dust prediction more precise by introducing sensitivity coefficients, enabling better response to different climate conditions and the diversity of construction equipment, and improving the accuracy of dust monitoring.

[0130] S24. Extract the proportion s of dust diffusion caused by dust-generating construction operations in the mechanical operation distribution dataset; it can accurately reflect the contribution of different mechanical operation types, intensities, and operation environments to dust diffusion. Based on this proportion, the distribution of dust sources can be further analyzed to optimize the dust control strategy, thereby reducing the impact of dust on the construction site and the surrounding environment.

[0131] S25. Extract the real-time mechanical operation frequency N m , construction duration T, dust emission E per single mechanical operation m , the proportion s of dust diffusion caused by dust-generating construction operations, and the dust diffusion correction factor F d , after dimensionless processing, the mechanical operation dust index Jyz of the i-th dust monitoring area is calculated through the following formula i :

[0132]

[0133] The dust diffusion ratio Mfb of material storage in the i-th dust monitoring area i The acquisition method includes the following steps:

[0134] S31. Detect the dust storage volume in the material storage area of the i-th dust monitoring area through a dust detection device to obtain the dust storage volume M d , and extract the proportion t of the area of exposed sand, cement, and coal ash materials in the i-th dust monitoring area in the diffusion dataset; obtaining the dust storage volume in the material storage area through a dust detection device and extracting the proportion t of the area of exposed sand, gravel, cement, and coal ash materials can effectively quantify the contribution of material storage to dust diffusion. The proportion of the area of exposed materials directly affects the potential of dust diffusion and further reflects the dust release ability of different materials, providing accurate data support for the dust prevention and control strategy.

[0135] S32. Measure the average diameter D of dust particles in the material storage area of the i-th dust monitoring area through a particle sensor p , in μm, and take the average value based on multiple measurements. The expression is as follows:

[0136]

[0137] where D r represents the diameter of dust particles measured at the r-th time, and g represents the total number of measurements; measuring the average diameter of dust particles through a particle sensor can determine the particle size of dust in the material storage area, which is crucial for predicting the behavior of dust diffusion. Larger particles usually settle faster, and smaller particles are more easily blown away by the wind. Therefore, measuring the average particle diameter can help evaluate the potential of different particles for dust diffusion and optimize dust control measures.

[0138] S33. Measure the boundary height H of the material storage area in the i-th dust monitoring area through an altimeter, specifically the height of the enclosure; by measuring the height H of the enclosure of the material storage area, the impact of the boundary facility on dust diffusion can be quantified. A higher enclosure can effectively reduce dust diffusion, especially in the case of strong winds. Through this measurement, the design of the dust prevention facilities in the material storage area can be optimized to reduce the impact of dust diffusion on the surrounding environment.

[0139] S34. Extract the dust storage M d , the proportion t of the area of exposed sand, gravel, cement, and coal ash materials, the average diameter D of dust particles p and the boundary height H of the material storage area. After dimensionless processing, calculate the dust diffusion ratio Mfb of the material storage in the i-th dust monitoring area through the following formula i :

[0140]

[0141] In the formula, when the dust storage M d and the proportion t of the exposed material area increase, Mfb i increases, reflecting an increase in the potential of dust diffusion. The larger the average diameter D of dust particles p and the boundary height H of the material storage area, the smaller Mfb i , indicating that a higher enclosure or larger particles help reduce dust diffusion.

[0142] In this embodiment, the present invention can accurately calculate the dust emission intensity based on real-time collected data (including wind speed, bare ground area, soil coverage rate, soil dry density, etc.), providing a multi-dimensional and dynamic basis for the monitoring and control of dust sources. The introduction and optimization of each step can enhance the accuracy of dust emission prediction and provide data support for subsequent dust prevention and control measures (such as watering, soil covering, environmental monitoring, etc.), effectively improving the efficiency and accuracy of dust management at the construction site.

[0143] And quantify the contribution of mechanical operations and material storage to dust diffusion. By combining multiple factors such as the operation intensity of construction machinery, air humidity, and the dust characteristics of material storage, the dust emission amount can be effectively evaluated and precise control suggestions can be provided. The introduction and optimization of each step not only enhance the accuracy of dust monitoring and evaluation but also provide detailed data support for the control of dust at the construction site, improving the scientificity and effectiveness of dust management.

[0144] Embodiment 5

[0145] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the dust evaluation module includes a first fitting unit and a first evaluation unit;

[0146] The first fitting unit is used to correlate the ground dust emission intensity Gds i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i in the i-th dust monitoring area to construct the regional dust comprehensive index Yczs i in the i-th dust monitoring area:

[0147] Yczs i = Gds i * w1 + Jyz i * w2 + Mfb i * w3;

[0148] In the formula, w1, w2, and w3 respectively represent the weights of the ground dust emission intensity Gds i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i in the i-th dust monitoring area, and w1 + w2 + w3 = 1;

[0149] The first evaluation unit is used to preset the dust risk threshold Cz and compare and evaluate the regional dust comprehensive index Yczs i in the i-th dust monitoring area with the dust risk threshold Cz to obtain a classification result, including:

[0150] When the regional dust comprehensive index Yczs of the i-th dust monitoring area i > the dust risk threshold Cz * 150%, it indicates that there is a dust risk in this dust monitoring area, and it is the first severe risk level;

[0151] When the dust risk threshold Cz * 120% ≤ the regional dust comprehensive index Yczs of the i-th dust monitoring area i ≤ the dust risk threshold Cz * 150%, it indicates that there is a dust risk in this dust monitoring area, and it is the second severe risk level, which is lower than the first severe risk level;

[0152] When the dust risk threshold Cz ≤ the regional dust comprehensive index Yczs of the i-th dust monitoring area i ≤ the dust risk threshold Cz * 120%, it indicates that there is a dust risk in this dust monitoring area, and it is the third severe risk level, which is lower than the second severe risk level;

[0153] When the regional dust comprehensive index Yczs of the i-th dust monitoring area i < the dust risk threshold Cz, it indicates that there is no dust risk in this dust monitoring area, and continuous monitoring is carried out.

[0154] In this embodiment, the ground dust intensity Gds of the i-th dust monitoring area i , the mechanical operation dust index Jyz i and the material storage dust diffusion ratio Mfb i are correlated to construct the regional dust comprehensive index Yczs of the i-th dust monitoring area i . Dust source characteristics, construction machinery use, material storage conditions, etc. in different monitoring areas will all affect the dust intensity. Combining these factors helps to accurately evaluate the overall situation of dust diffusion. By assigning appropriate weights to each factor, it can ensure that the model is more suitable for the actual environment and working conditions, and provide a more accurate dust assessment.

[0155] By setting the dust risk threshold Cz and conducting hierarchical evaluation, it is possible to distinguish different dust risk levels. Using hierarchical risk assessment (such as the first severe risk level, the second severe risk level, and the third severe risk level), it is possible to judge the specific situation of dust risk based on the dust comprehensive index of different monitoring areas. This hierarchical mechanism can not only help identify the most urgent risk areas and take treatment measures in a timely manner, but also provide clear work priorities for daily management.

[0156] Example 6

[0157] This embodiment is an explanatory description carried out in Example 5. Please refer to Figure 1, specifically, the intelligent management module includes a first policy unit and a policy correction unit;

[0158] The first policy unit is used to generate corresponding control strategies according to the grading results generated by the first evaluation unit, including:

[0159] Generating the first control strategy according to the first severe risk level includes: setting 3 additional sprinkler trucks or high-pressure spray devices in the head monitoring area, and controlling the sprinkling frequency distribution in different time periods, including:

[0160] Morning: 6:00 - 9:00, 2 times per hour;

[0161] Noon: 12:00 - 14:00, 3 times per hour;

[0162] Evening: 17:00 - 20:00, 2 times per hour;

[0163] Increasing the boundary height H of the current 15% - 30% of the material storage area in the head monitoring area, and setting dust-proof nets to cover the materials in the material storage area to ensure a dust-proof net coverage area of 50%; reducing the current real-time operation frequency of machinery by 15%;

[0164] Generating the second control strategy according to the second severe risk level includes: setting 2 additional sprinkler trucks or high-pressure spray devices in the head monitoring area, and controlling the sprinkling frequency distribution in different time periods, including:

[0165] Morning: 6:00 - 9:00, 1 time per hour;

[0166] Noon: 12:00 - 14:00, 2 times per hour;

[0167] Evening: 17:00 - 20:00, 1 time per hour;

[0168] Increasing the boundary height H of the current 8% - 14% of the material storage area in the head monitoring area, and setting dust-proof nets to cover the materials in the material storage area to ensure a dust-proof net coverage area of 30 - 49%; reducing the current real-time operation frequency of machinery by 10%;

[0169] Generating the third control strategy according to the third severe risk level includes: setting 1 additional sprinkler truck or high-pressure spray device in the head monitoring area, and controlling the sprinkling frequency distribution in different time periods, including:

[0170] Morning: 6:00 - 9:00, 1 time every 1.5 hours;

[0171] Noon: 12:00 - 14:00, 2 times every 1.5 hours;

[0172] Evening: 17:00 - 20:00, once every 1.5 hours;

[0173] Increase the boundary height H of the current 3% - 7% material storage area in this head monitoring area, and set up dust-proof nets to cover the materials in the material storage area to ensure that the dust-proof net coverage area reaches 20 - 29%; reduce the current real-time operation frequency of machinery by 5%.

[0174] The strategy correction unit is used to recalculate and obtain the comprehensive dust emission index Yczs of the i-th dust emission monitoring area after implementing the first regulation strategy, the second regulation strategy, and the third regulation strategy. i Then repeat the steps of the dust emission monitoring module and the dust emission evaluation module. If the classification result of the first evaluation unit changes, the strategy correction unit automatically upgrades or downgrades the regulation strategy.

[0175] In this embodiment, based on the dust emission risk classification result generated by the first evaluation unit, the intelligent management module can formulate corresponding regulation strategies according to different risk levels. This hierarchical management can ensure that different dust emission control measures are taken for different risk levels, avoiding the situations of "over-control" or "under-control", and thus realizing the reasonable allocation and optimal use of resources. For the first severe risk level, which represents the dust emission monitoring area with the highest risk level, the strictest control measures are formulated, such as increasing the number of sprinkler trucks or high-pressure spray devices, increasing the dust-proof net coverage, and taking sprinkling at a higher frequency. Through these measures, the spread of dust is effectively reduced, ensuring the air quality and construction environment in the area, and reducing the harm of dust to the surrounding environment and personnel. Strong dust emission control measures, such as increasing the number of sprinkler trucks and high-pressure spray devices and sprinkling at a high frequency during key periods, can effectively inhibit the flying and spreading of dust particles, timely improve the air quality, and reduce environmental pollution.

[0176] For the dust emission monitoring areas with the second severe risk level, relatively moderate measures are taken, such as moderately increasing the number of sprinkler trucks and the spraying frequency, and appropriately increasing the dust-proof net coverage area of the material storage area. Such a strategy can effectively balance the cost and effect, ensure that the spread of dust can still be controlled during high-risk periods, but at the same time avoid waste of resources. Adjusting the control intensity according to different risk levels provides flexibility for on-site management, enabling the regulation measures to adapt to different working environments and construction conditions in real time.

[0177] For the lower dust risk level, which is the third most serious risk level, the strategy is adjusted to mild intervention, such as appropriately increasing the number of sprinklers and reducing the frequency of mechanical operations. Although the dust risk is low, these mild measures can still be used to continuously monitor and reduce the spread of dust. This can maintain appropriate risk prevention and control measures, ensure that overall dust management is not relaxed, and avoid local dust accumulation. Compared with high-risk areas, the governance measures in the third-level risk areas are relatively less in terms of resource investment, which helps save costs while maintaining governance effects.

[0178] Based on real-time monitoring data and dust changes, the strategy correction unit can dynamically adjust the existing control strategy. This flexible adjustment mechanism can ensure timely adjustment of control measures in case of environmental changes or emergencies. For example, in extreme weather conditions, it may be necessary to increase the frequency of watering or deploy more equipment to ensure continuous and effective dust control.

[0179] Example 7

[0180] See also Figure 2 Specifically, a construction project management method includes the following steps:

[0181] Step 1: Pre-select a construction site for a building project and divide the construction site into several dust monitoring areas, and set up monitoring points in the dust monitoring areas;

[0182] Step 2: Monitor and record the dust-related parameter information generated during the construction of several dust monitoring areas, and construct a diffusion data set and a mechanical operation distribution data set;

[0183] The dust source distribution model is constructed and trained using neural network technology, and the diffusion data set and mechanical operation distribution data set are deeply calculated to obtain the ground dust intensity Gds in the i-th dust monitoring area. i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i ;

[0184] Step 3: Used to calculate the ground dust intensity Gds in the i-th dust monitoring area i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i Correlate to construct the regional dust comprehensive index Yczs of the i-th dust monitoring area i ; And preset the dust risk threshold Cz to generate the regional dust comprehensive index Yczs of the i-th dust monitoring area i Comparison and classification results with the dust risk threshold Cz;

[0185] Step 4. Dynamically adjust the dust control measures in the corresponding area according to the comparison and grading results, including automatic sprinkling, setting up dust-proof nets or optimizing the construction plan.

[0186] In this embodiment, through Step 1 and Step 4, dust monitoring points are set up in the construction site to monitor the dust-related parameters of multiple dust monitoring areas in real time. Combining the diffusion data set and the mechanical operation distribution data set, the neural network technology is used to deeply calculate the data to accurately obtain the ground dust intensity, the mechanical operation dust index and the material storage dust diffusion ratio of each dust monitoring area. And the regional dust comprehensive index Yczs of the i-th dust monitoring area is obtained by association. i After the post-evaluation, the risk grading result is automatically generated. This data-based intelligent evaluation avoids the errors of manual estimation and improves the accuracy of risk warning. According to the evaluation result, the system can automatically adjust the dust control measures, such as sprinkling water, setting up dust-proof nets or adjusting the construction plan, to ensure that reasonable control means are taken in areas with different risk levels, further reducing the generation of dust pollution and effectively protecting the environment and the health of construction workers. Through intelligent regulation, the resource input at the construction site is optimized, avoiding over-treatment or resource waste, and effectively saving costs. At the same time, the dynamic adjustment of control measures ensures the flexibility and pertinence of dust management, thus improving the construction efficiency.

[0187] The setting of the threshold value is for the convenience of comparison. Regarding the size of the threshold value, it depends on the amount of sample data and the number of base numbers set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0188] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formulas are set by those skilled in the art according to the actual situation. As mentioned above, only the preferred specific embodiments of the present invention are described, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A construction project management system, characterized in that: Including regional division module, dust monitoring module, dust assessment module and intelligent management module; The area division module is used to pre-select a construction site for a building project and divide the construction site into a number of dust monitoring areas, while setting monitoring points in the dust monitoring areas; The dust monitoring module is used to monitor and record the dust-related parameter information generated during the construction of the building project in several dust monitoring areas, and to construct a diffusion data set and a mechanical operation distribution data set; The dust source distribution model is constructed and trained using neural network technology, and the diffusion data set and mechanical operation distribution data set are deeply calculated to obtain the ground dust intensity Gds in the i-th dust monitoring area. i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i ; The dust assessment module is used to calculate the ground dust intensity Gds in the i-th dust monitoring area. i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i Correlate to construct the regional dust comprehensive index Yczs of the i-th dust monitoring area i ; And preset the dust risk threshold Cz to generate the regional dust comprehensive index Yczs of the i-th dust monitoring area i Comparison and classification results with the dust risk threshold Cz; The intelligent management module is used to dynamically adjust the dust control measures in the corresponding area according to the comparison and classification results, including automatic watering, setting up dust prevention nets or optimizing construction plans.

2. A construction project management system according to claim 1, characterized in that: The area division module includes a construction site data acquisition unit and a monitoring area division unit; The construction site data acquisition unit is used to collect the geometric shape, topography, elevation data of the construction site and the distribution information of construction equipment and materials by using drone aerial photography, three-dimensional laser scanning or geographic information system (GIS) technology to form a dust diffusion potential model of the construction site; The monitoring area division unit is used to divide the construction site into a number of dust monitoring areas at equal intervals, and visually mark them as Q1, Q2, Q3, ..., Q n , n represents the total number of dust monitoring areas.

3. A construction project management system according to claim 1, characterized in that: The dust monitoring module includes a first monitoring unit and a first calculation unit; The first monitoring unit is used to monitor and record material storage distribution and mechanical operation distribution information in a number of dust monitoring areas, and to establish a diffusion data set and a mechanical operation distribution data set; The expression of the diffusion data set is to are the management data of sand, cement and fly ash materials in the first to nth dust monitoring areas, respectively, and t represents the area ratio of exposed sand, cement and fly ash materials; The material management data of sand, gravel, cement and fly ash in the dust monitoring area include material name, material proportion and material exposed area; The expression of the mechanical operation distribution data set is: to They are the dust construction operation distribution data of the first to the nth dust monitoring areas, including the soil disturbance data of excavators and bulldozers, the dust distribution data generated by demolishing walls or cement blocks, and the dust distribution data during drilling or cutting operations; s represents the proportion of dust diffusion caused by dust construction operations.

4. A construction project management system according to claim 3, characterized in that: The first computing unit is used to perform denoising, stratification and storage processing on the diffusion data set and the mechanical operation distribution data set by using big data analysis technology, and to construct a dust source distribution model by using a neural network model CNN technology; After training the dust source distribution model, the diffusion data set and the mechanical operation distribution data set are deeply analyzed and calculated to obtain: the ground dust intensity Gds of the i-th dust monitoring area i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i ; The ground dust intensity Gds of the i-th dust monitoring area i The method of obtaining includes the following steps: S11. Collect real-time wind speed V through anemometer w , and use the following formula to calculate the wind speed correction factor W: W=(V w / V c ) u ; Where V c is the critical wind speed threshold, u is the particle sensitivity coefficient, which is set to u = 2; S12. Use ground remote sensing monitoring equipment, including drones or satellite images, to collect and calculate the exposed ground area A of the ith dust monitoring area in real time, and obtain the soil coverage rate C through soil cover monitoring sensors or image processing technology. f The soil coverage is expressed as a ratio, ranging from 0 to 1. S13, collecting soil samples in the i-th dust monitoring area by using a soil sampling device, and measuring the soil dry density ρ using a soil dryness tester; S14, the wind speed correction coefficient W, the exposed ground area A, and the soil coverage C collected or calculated in S11-S13 are f After dimensionless processing, the ground dust intensity Gds in the i-th dust monitoring area is calculated by the following formula: i : Gds i =A*ρ*(1―C f )*W*V; In the formula, A represents the exposed ground area, ρ represents the dry density of soil, and C f represents soil coverage, W represents wind speed correction coefficient, and V represents the instantaneous wind speed measured at the monitoring point.

5. A construction project management system according to claim 1, characterized in that: The mechanical operation dust index Jyz of the i-th dust monitoring area i The method of obtaining includes the following steps: S21. Count the real-time operation frequency N of the machinery in the i-th dust monitoring area through dust monitoring equipment or construction logs. m , the unit is kg / event, and the construction duration T is recorded; S22. According to the type of construction machinery and the intensity of operation, the dust emission E of a single mechanical operation is searched or calculated through the dust emission coefficient database. m , including dust emissions from different types of construction machinery, including excavators, bulldozers and rollers, determined by experimental data or standard parameters; S23. By collecting the weight of each construction machine and the air humidity, the dust diffusion correction factor F is calculated using the following formula: d : Where k is the initial proportionality factor of the correction factor, which depends on the experimental calibration and has a typical value of 1. i represents the total weight of construction machinery in the i-th construction monitoring area, Z ref Indicates the maximum mechanical weight threshold, H α represents air humidity, m represents the mechanical weight sensitivity coefficient, m=1.5; j represents the air humidity sensitivity coefficient, j=2; S24, extract the proportion of dust diffusion caused by dusty construction work in the mechanical operation distribution data set s; S25, extracting the mechanical real-time operating frequency N in S21-S25 m , construction duration T, dust emission from a single mechanical operation E m , the proportion of dust diffusion caused by dusty construction work s and the dust diffusion correction factor F d After dimensionless processing, the mechanical operation dust index Jyz of the i-th dust monitoring area is calculated by the following formula: i :

6. A construction project management system according to claim 3, characterized in that: The material storage dust diffusion ratio Mfb of the i-th dust monitoring area i The acquisition method includes the following steps: S31. Detect the dust reserve in the material storage area of ​​the i-th dust monitoring area through the dust detection equipment to obtain the dust reserve M d , and extract the exposed sand, cement and fly ash material area proportion t of the i-th dust monitoring area in the diffusion data set; S32, measuring the average diameter D of dust particles in the material storage area of ​​the i-th dust monitoring area through a particle sensor p , in μm, is an average value based on multiple measurements and is expressed as follows: Among them, D r represents the dust particle diameter measured for the rth time, and g represents the total number of measurements; S33, measuring the boundary height H of the material storage area of ​​the i-th dust monitoring area by a height measuring instrument, specifically the height of the enclosure; S34, extract the dust reserves M in S31-S33 d , the proportion of exposed sand, cement and fly ash materials area t, the average diameter of dust particles D p And the boundary height H of the material storage area, after dimensionless processing, the material storage dust diffusion ratio Mfb of the i-th dust monitoring area is calculated by the following formula: i : In the formula, when the dust storage M d When the exposed material area ratio t increases, Mfb i Increase, reflecting the potential of dust diffusion increased, the average diameter of dust particles D p The larger the boundary height H of the material storage area, the greater the Mfb i The smaller it is, the higher the enclosure or the larger the particles will help reduce dust dispersion.

7. A construction project management system according to claim 1, characterized in that: The dust assessment module includes a first fitting unit and a first assessment unit; The first fitting unit is used to convert the ground dust intensity Gds of the i-th dust monitoring area into i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i Correlate to construct the regional dust comprehensive index Yczs of the i-th dust monitoring area i : <h2 style=";text-align:left;direction:ltr">Yczs<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =Gds<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> *w1+Jyz<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> *w2+Mfb<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> *w3; Where w1, w2 and w3 represent the ground dust intensity Gds in the ith dust monitoring area, respectively. i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i The weight of , and w1+w2+w3=1; The first evaluation unit is used to preset the dust risk threshold Cz and calculate the regional dust comprehensive index Yczs of the i-th dust monitoring area. i Compare and evaluate with the dust risk threshold Cz to obtain the classification results, including: When the regional dust comprehensive index Yczs of the i-th dust monitoring area i > Dust risk threshold Cz*150%, indicating that there is dust risk in the dust monitoring area and it is the first serious risk level; When the dust risk threshold Cz*120% ≤ the regional dust comprehensive index Yczs of the ith dust monitoring area i ≤Dust risk threshold Cz*150%, indicating that there is dust risk in the dust monitoring area, and it is the second most serious risk level, which is lower than the first most serious risk level; When the dust risk threshold Cz≤the regional dust comprehensive index Yczs of the ith dust monitoring area i ≤Dust risk threshold Cz*120%, indicating that the dust monitoring area has dust risk and is the third most serious risk level, which is lower than the second most serious risk level; When the regional dust comprehensive index Yczs of the i-th dust monitoring area i <Dust risk threshold Cz, indicating that there is no dust risk in the dust monitoring area, and continuous monitoring is required.

8. A construction project management system according to claim 7, characterized in that: The smart management module includes a first strategy unit and a strategy modification unit; The first strategy unit is used to generate a corresponding control strategy according to the classification result generated by the first evaluation unit, including: The first control strategy generated according to the first serious risk level includes: setting up 3 additional sprinkler trucks or high-pressure spray devices in the head monitoring area, and controlling the distribution of sprinkler frequency in different time periods, including: Morning: 6:00-9:00, twice every hour; Lunch: 12:00-14:00, 3 times per hour; Evening: 17:00-20:00, twice every hour; Increase the current 15%-30% material storage area boundary height H in the lift monitoring area, and set up dust-proof nets to cover the materials in the material storage area to ensure that 50% of the dust-proof net coverage area is reached; reduce the current mechanical real-time operation frequency by 15%; The second control strategy generated according to the second most serious risk level includes: setting up two additional sprinkler trucks or high-pressure spray devices in the head monitoring area, and controlling the distribution of sprinkler frequency in different time periods, including: Morning: 6:00-9:00, once every hour; Lunch: 12:00-14:00, twice every hour; Evening: 17:00-20:00, once every hour; Increase the boundary height H of the material storage area in the head monitoring area from the current 8%-14%, and set up dust-proof nets to cover the materials in the material storage area to ensure that the dust-proof net coverage area reaches 30-49%; reduce the current real-time operation frequency of the machine by 10%; The third control strategy generated according to the third serious risk level includes: setting up an additional sprinkler truck or high-pressure spray device in the head monitoring area, and controlling the distribution of sprinkler frequency in different time periods, including: Morning: 6:00-9:00, once every 1.5 hours; Lunch: 12:00-14:00, 2 times every 1.5 hours; Evening: 17:00-20:00, once every 1.5 hours; Increase the current 3%-7% material storage area boundary height H in the lift monitoring area, and set a dustproof net to cover the materials in the material storage area to ensure that the dustproof net coverage area reaches 20-29%; reduce the current mechanical real-time operation frequency by 5%.

9. A construction project management system according to claim 8, characterized in that: The strategy correction unit is used to recalculate and obtain the regional dust comprehensive index Yczs of the i-th dust monitoring area after executing the first control strategy, the second control strategy and the third control strategy. i Then, the steps of the dust monitoring module and the dust assessment module are repeated. If the classification result of the first assessment unit changes, the strategy correction unit automatically upgrades or downgrades the control strategy.

10. A construction project management method, applied to a construction project management system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Pre-select a construction site for a building project and divide the construction site into several dust monitoring areas, and set up monitoring points in the dust monitoring areas; Step 2: Monitor and record the dust-related parameter information generated during the construction of several dust monitoring areas, and construct a diffusion data set and a mechanical operation distribution data set; The dust source distribution model is constructed and trained using neural network technology, and the diffusion data set and mechanical operation distribution data set are deeply calculated to obtain the ground dust intensity Gds in the i-th dust monitoring area. i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i ; Step 3: Used to calculate the ground dust intensity Gds in the i-th dust monitoring area i , Mechanical operation dust index Jyz i and material storage dust diffusion ratio Mfb i Correlate to construct the regional dust comprehensive index Yczs of the i-th dust monitoring area i ; And preset the dust risk threshold Cz to generate the regional dust comprehensive index Yczs of the i-th dust monitoring area i Comparison and classification results with the dust risk threshold Cz; Step 4: Dynamically adjust dust control measures in the corresponding area based on the comparative classification results, including automatic watering, setting up dust nets or optimizing construction plans.

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