A municipal road construction decision-making method and system

Abstract

The application relates to the technical field of intelligent management, and particularly discloses a municipal road construction decision method and system, which comprises the following steps: collecting video data through a monitoring device arranged at a construction site, collecting wind speed data and humidity data through an environmental sensor; identifying a construction operation type from the video data, evaluating dust generation intensity based on the identified construction operation type, the spatio-temporal distribution characteristics, the wind speed data and the humidity data; predicting dust deposition amounts of lenses of each monitoring device according to the dust generation intensity and position information of the monitoring device; and generating a cleaning decision scheme for the monitoring device based on a time sequence change curve of the dust deposition amounts. The application can realize dust intensity evaluation of a construction site, dust deposition prediction of a monitoring device and cleaning decision generation, guarantee clear and continuous monitoring pictures, improve supervision reliability and reduce cleaning resource waste.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, specifically to a method and system for decision-making in municipal road construction. Background Technology

[0002] Municipal road construction is a crucial part of urban infrastructure development, impacting smooth traffic flow and the travel experience of citizens. Before construction, it is essential to complete surveying and design, plan approval, and traffic diversion planning, clarifying key processes such as pipeline relocation and roadbed treatment. During construction, strict quality control must be maintained for processes such as roadbed compaction and asphalt paving, while simultaneously implementing environmental protection measures such as dust control and noise management to minimize the impact on the lives of surrounding residents.

[0003] During municipal road construction, project management typically uses video surveillance equipment to monitor the construction site in real time to track progress, standardize operations, and ensure safety. However, construction site operations frequently involve excavation, transportation, and material storage, generating significant amounts of dust. This dust disperses in the air and adheres to the lens of the camera equipment, causing it to become obscured, contaminated, or even blurred.

[0004] This situation directly leads to a decrease in the clarity of the surveillance footage, resulting in blurry images and a limited field of view, which in turn affects the supervisory personnel's judgment and oversight of the construction situation. In severe cases, it may also prevent the timely detection of construction safety hazards and violations, thus reducing the practical value of video surveillance. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for decision-making in municipal road construction, thereby solving the aforementioned technical problems.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for making decisions on municipal road construction includes the following steps:

[0008] Video data is collected through monitoring equipment deployed at the construction site, and wind speed and humidity data are collected through environmental sensors.

[0009] The types of construction operations, including earthwork excavation, material transportation and stockpiling, are identified from video data. Based on the identified types of construction operations and their spatiotemporal distribution characteristics, combined with wind speed data and humidity data, the intensity of dust generation is assessed.

[0010] Based on the dust generation intensity and the location information of the monitoring equipment, the amount of dust deposited on the lenses of each monitoring device is predicted; based on the time-series variation curve of the dust deposit amount, a cleaning decision plan for the monitoring equipment is generated.

[0011] As a further aspect of the present invention, it includes the following steps:

[0012] Video data is collected through monitoring equipment deployed at the construction site, and wind speed and humidity data are collected through environmental sensors.

[0013] The types of construction operations, including earthwork excavation, material transportation and stockpiling, are identified from video data. Based on the identified types of construction operations and their spatiotemporal distribution characteristics, combined with wind speed data and humidity data, the intensity of dust generation is assessed.

[0014] Based on the dust generation intensity and the location information of the monitoring equipment, the amount of dust deposited on the lenses of each monitoring device is predicted; based on the time-series variation curve of the dust deposit amount, a cleaning decision plan for the monitoring equipment is generated.

[0015] As a further aspect of the present invention: assessing dust generation intensity includes:

[0016] The amount of dust generated by the foundation is determined according to the type of construction operation. The amount of dust generated by the foundation is based on historical measurement data and a dust release reference value is preset for each type of operation within a unit time.

[0017] Wind speed and humidity data are introduced as environmental correction factors, with wind speed data used to calculate the dust diffusion rate and humidity data used to calculate the dust settling rate.

[0018] A multivariate regression model was used to comprehensively calculate the basic dust generation, diffusion rate, and settling rate. The coefficients of the multivariate regression model were calibrated using on-site measured data, and the calculation result is the dust generation intensity that characterizes the overall dust situation at the construction site.

[0019] As a further aspect of the present invention: predicting dust deposition includes:

[0020] The distance attenuation component is calculated based on the straight-line distance between the monitoring equipment and the location where the dust occurs. The greater the distance, the smaller the distance attenuation component. The azimuth shielding component is calculated based on the relative azimuth angle between the monitoring equipment and the location where the dust occurs. The azimuth shielding component depends on the degree of obstruction by obstacles in the line of sight.

[0021] Multiply the distance attenuation component by the azimuth shading component to obtain the spatial attenuation factor; calculate the cosine of the angle between the real-time wind speed vector and the normal direction of the monitoring equipment lens to obtain the wind direction influence factor.

[0022] The effective dust intensity at the monitoring equipment location is obtained by multiplying the dust generation intensity, spatial attenuation factor, and wind direction influence factor. Based on the effective dust intensity, the amount of dust deposition is calculated using a deposition kinetic model that considers the gravitational settling and inertial collision effects of particles.

[0023] As a further aspect of the present invention: the time-series variation curve is constructed using continuous time series dust deposition data, with the horizontal axis representing time and the vertical axis representing cumulative deposition.

[0024] The curve characteristics of the time-series variation curves are collected. The curve characteristics include deposition rate and acceleration. The deposition rate is calculated by the change in the amount of deposition per unit time, and the acceleration is calculated by the second derivative of the deposition rate.

[0025] As a further aspect of the present invention: the cleaning decision scheme includes cleaning priority determination and execution timing determination. The cleaning priority is determined based on the current value of dust deposition and deposition rate, and the execution timing is determined based on acceleration and construction plan arrangement.

[0026] The scheme generation process uses a decision tree algorithm, and the branching conditions of the decision tree include different combinations of deposition threshold, rate threshold and acceleration threshold.

[0027] As a further aspect of the present invention: the specific content of the cleaning decision scheme includes the target equipment identification, the suggested cleaning time and the estimated operation time. The target equipment identification is determined by the monitoring equipment number, the suggested cleaning time is determined by coordinating the construction interval and weather conditions, and the estimated operation time is calculated based on the amount of dust accumulation and the cleaning process standards.

[0028] The cleaning decision solution is output in the form of a structured data packet, which is transmitted to the mobile cleaning terminal via a wireless communication network.

[0029] A municipal road construction decision-making system includes:

[0030] Data Acquisition Module: Collects video data through monitoring equipment deployed at the construction site, and collects wind speed and humidity data through environmental sensors;

[0031] Analysis module: Identifies construction operation types from video data, including earthwork excavation, material transportation and stockpiling operations. Based on the identified construction operation types and their spatiotemporal distribution characteristics, combined with wind speed data and humidity data, the intensity of dust generation is assessed.

[0032] Decision module: Based on the dust generation intensity and the location information of the monitoring equipment, predict the amount of dust deposited on the lenses of each monitoring device; based on the time-series change curve of the dust deposit amount, generate a cleaning decision plan for the monitoring equipment.

[0033] The beneficial effects of this invention compared to the prior art are as follows:

[0034] 1) This invention can effectively identify different types of operations at construction sites and, in combination with their spatial location and temporal characteristics, establish a dust generation assessment mechanism associated with environmental factors; it can analyze the changing trends of dust in a dynamic construction environment in real time, avoiding the shortcomings of making rough judgments based solely on experience, and enabling construction managers to have a clearer, more continuous and objective understanding of the dust situation on site, thereby providing a reliable basis for subsequent decision-making.

[0035] 2) Based on the dust generation intensity, this invention can further calculate the dust deposition at the location of construction monitoring equipment and establish a deposition change curve through continuous time series. This not only reflects the accumulation trend of dust on the lens surface of the monitoring equipment, but also reveals the change characteristics such as deposition rate and acceleration. This method can provide an intuitive basis for subsequent cleaning arrangements, enabling the cleaning work to change from passive response to active prediction, thereby improving the continuous clarity of the monitoring image.

[0036] 3) This invention can automatically generate cleaning decision schemes based on the key features of the dust deposition curve and transmit them to the cleaning terminal in a structured manner. The scheme clarifies the cleaning object, the timing of the operation, and the expected operation duration, so that the cleaning process can be reasonably connected with the construction schedule and avoid disorder or over-cleaning. This not only ensures the stable availability of the monitoring screen, but also reduces blind spots and delays in the construction safety supervision process, ensuring that the management can continuously grasp the real status of the site. Attached Figure Description

[0037] The invention will now be further described with reference to the accompanying drawings.

[0038] Figure 1 This is a flowchart illustrating a municipal road construction decision-making method according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 As shown, this invention is a method for making decisions on municipal road construction, comprising the following steps:

[0041] Video data is collected through monitoring equipment deployed at the construction site, and wind speed and humidity data are collected through environmental sensors.

[0042] The types of construction operations, including earthwork excavation, material transportation and stockpiling, are identified from video data. Based on the identified types of construction operations and their spatiotemporal distribution characteristics, combined with wind speed data and humidity data, the intensity of dust generation is assessed.

[0043] In a preferred embodiment of the present invention, identifying the construction operation type includes:

[0044] Identify the movement trajectory of the bucket of excavating machinery as a characteristic of earthwork excavation operations; identify the movement path and loading / unloading behavior of transport vehicles as a characteristic of material transportation operations; identify the rate of volume change of material piles as a characteristic of stockpiling operations.

[0045] The spatiotemporal distribution characteristics are characterized by recording the location, duration, and impact range of various operations. The location is determined by the latitude and longitude coordinates of the monitoring equipment, the duration is calculated by the video timestamp, and the impact range is obtained by measuring the pixel area of ​​the operation area using image recognition technology.

[0046] Specifically, when excavating machinery is carrying out earthwork operations, the movement trajectory of its bucket in space exhibits a regular arc or straight line motion. This trajectory change can clearly reflect that the machinery is carrying out excavation operations; by continuously capturing and identifying this motion characteristic, it can be determined that earthwork excavation operations have occurred on site.

[0047] In the material transportation process, the movement path of transport vehicles on the construction site has a clear starting point and ending point, and loading or unloading will occur after arriving at the designated area. The continuous change trajectory of the vehicle's position and the situation of stopping in a specific area and changing the cargo can characterize the progress of the transportation operation.

[0048] For material stacking operations, the shape of the stacking area is shown in video images as the phenomenon of volume gradually increasing or decreasing over time. By using image processing methods to identify the rate of change of the outline of the stack, the state of the material during stacking or transfer can be determined.

[0049] By identifying the above-mentioned multiple types of operations, we can further extract spatiotemporal distribution features, thereby achieving an accurate representation of construction site operations.

[0050] In this process, it is necessary to record the specific location of each type of operation in space. The location is determined by the latitude and longitude coordinates of the images captured by the monitoring equipment. The duration is determined by comparing and analyzing the timestamps of the video frames to mark the start and end times of a certain operation, thereby obtaining the duration of the operation. The scope of the operation is obtained by extracting the outline boundary of the operation area through image recognition technology and converting the pixel area of ​​the area in the image into the actual spatial range.

[0051] In another preferred embodiment of the present invention, assessing dust generation intensity includes:

[0052] The amount of dust generated by the foundation is determined according to the type of construction operation. The amount of dust generated by the foundation is based on historical measurement data and a dust release reference value is preset for each type of operation within a unit time.

[0053] Wind speed and humidity data are introduced as environmental correction factors, with wind speed data used to calculate the dust diffusion rate and humidity data used to calculate the dust settling rate.

[0054] A multivariate regression model was used to comprehensively calculate the basic dust generation, diffusion rate, and settling rate. The coefficients of the multivariate regression model were calibrated using on-site measured data, and the calculation result is the dust generation intensity that characterizes the overall dust situation at the construction site.

[0055] Specifically, the process of assessing dust generation intensity is carried out along the main lines of source strength determination, environmental action characterization, and comprehensive modeling. The amount of basic dust generation is determined based on the type of construction operation. This is because the differences in material disturbance, contact crushing, and surface turning over in different operations lead to different abilities of particles to be scattered and resuspended. Historical measurement data records the stable release level per unit time under typical working conditions. Using this reference value as the source term can provide a reproducible benchmark for subsequent calculations.

[0056] Wind speed data is used to characterize the diffusion process; airflow carries both horizontal transport and superimposed turbulent mixing, and particles are more easily carried away from the source region and diluted over a larger area under stronger airflow; a feasible method is to correlate instantaneous wind speed and its variation within a time window with near-surface mixing intensity to construct a diffusion efficiency index obtained by wind speed mapping, which reflects the degree of attenuation of the source term by transport and mixing.

[0057] Humidity data is used to characterize the sedimentation process because air humidity affects the hygroscopic growth and aggregation of particles. Particle size and adhesion tendency change with humidity conditions. The formation of a water film makes it easier for particles to be retained after contact with the surface and reduces resuspension. Specifically, the empirical relationship between relative humidity and particle hygroscopicity and surface adhesion probability can be linked to obtain a sedimentation rate index mapped from humidity, which describes the tendency of particles to return from the air to the surface.

[0058] After defining the physical meaning and value methods of the three quantities—source intensity, diffusion, and deposition—a multivariate regression model is introduced for comprehensive calculation. The principle is to simultaneously characterize the main effects and interactions of the three on dust conditions through statistical learning. The model coefficients are calibrated using field measurement data to ensure that the output closely matches the actual changes at the construction site. The regression results serve as the dust generation intensity, reflecting the overall dust conditions within a certain spatiotemporal scale after being excited by the source term, diluted by the wind field, and promoted by humidity. This provides a unified quantitative basis for subsequent deposition prediction and cleanup decisions.

[0059] Based on the dust generation intensity and the location information of the monitoring equipment, the amount of dust deposited on the lenses of each monitoring device is predicted; based on the time-series variation curve of the dust deposit amount, a cleaning decision plan for the monitoring equipment is generated.

[0060] In another preferred embodiment of the present invention, predicting the amount of dust deposition includes:

[0061] The distance attenuation component is calculated based on the straight-line distance between the monitoring equipment and the location where the dust occurs. The greater the distance, the smaller the distance attenuation component. The azimuth shielding component is calculated based on the relative azimuth angle between the monitoring equipment and the location where the dust occurs. The azimuth shielding component depends on the degree of obstruction by obstacles in the line of sight.

[0062] Multiply the distance attenuation component by the azimuth shading component to obtain the spatial attenuation factor; calculate the cosine of the angle between the real-time wind speed vector and the normal direction of the monitoring equipment lens to obtain the wind direction influence factor.

[0063] The effective dust intensity at the location of the monitoring equipment is obtained by multiplying the dust generation intensity, spatial attenuation factor, and wind direction influence factor. Based on the effective dust intensity, the amount of dust deposition is calculated using a deposition kinetic model that considers the gravitational settling and inertial collision effects of particles.

[0064] Specifically, the prediction of dust deposition relies on the propagation attenuation and deposition process of particles from the source area to the location of the monitoring equipment. The straight-line distance between the monitoring equipment and the dust generation point reflects the physical law of continuous diffusion and dilution of aerosol particles during spatial transmission. The longer the distance, the more widely the particles carried by the air mass are distributed, and the concentration level received per unit area decreases accordingly. Therefore, the degree of concentration attenuation as the propagation path increases can be characterized by distance. The specific correspondence can be calibrated through experiments.

[0065] The relative azimuth between the monitoring equipment and the dust source reflects the matching between the particle movement direction and the line of sight. If there are obstacles such as buildings, machinery or piles in the propagation path, the dust-laden airflow will be partially blocked or its flow trajectory will be changed. As a result, the particle flux entering the lens direction will be weakened. The higher the degree of obstruction, the smaller the contribution to deposition.

[0066] Distance attenuation and azimuth occlusion are independent geometric and occlusion factors. Combining the two can more realistically characterize the attenuation of particles on the spatial arrival path. The result of this combination is the spatial attenuation factor.

[0067] The geometric relationship between the airflow direction and the lens surface also has a direct impact on particle deposition. The angle between the real-time wind speed vector and the lens normal direction determines the size of the airflow projection on the vertical surface. When the angle is close to being directly opposite, particles are more likely to reach the lens with the airflow. When the angle deviates, more components slide along the tangential direction, reducing the effective impact. The wind direction influence factor obtained by calculating the cosine value of the angle can intuitively represent this geometric relationship.

[0068] Multiplying the dust generation intensity by the spatial attenuation factor and the wind direction influence factor yields the effective dust intensity actually reaching the monitoring equipment. This intensity includes both the source term release level and the comprehensive modulation of the transmission process by spatial diffusion, obstacle obstruction, and wind direction relationship.

[0069] After obtaining the effective intensity, it is necessary to further consider the deposition mechanism of particles on the lens surface. The deposition kinetics model can describe the behavior of different particle sizes under the influence of airflow and gravity. Particles are continuously driven by downward sedimentation in the gravitational field, and the closer they are to the surface, the easier it is for them to come into contact with and deposit. At the same time, particles cannot completely follow the streamline due to their own inertia in high-speed airflow and directly collide with the lens surface. This inertial collision is also an important deposition mechanism. By combining the effective intensity with the deposition kinetics model, the amount of dust deposited on the lens that accumulates within a certain time range can be calculated, providing data support for subsequent cleaning decisions. The deposition kinetics model is now relatively complete, and this is just a routine application, so it will not be elaborated on further.

[0070] In another preferred embodiment of the present invention, the time-series variation curve is constructed using continuous time-series dust deposition data, with the horizontal axis representing time and the vertical axis representing cumulative deposition.

[0071] The curve characteristics of the time-series variation curves are collected. The curve characteristics include deposition rate and acceleration. The deposition rate is calculated by the change in the amount of deposition per unit time, and the acceleration is calculated by the second derivative of the deposition rate.

[0072] In a preferred embodiment, the cleaning decision-making scheme includes cleaning priority determination and execution timing determination. The cleaning priority is determined based on the current value of dust deposition and deposition rate, and the execution timing is determined based on acceleration and construction plan arrangement.

[0073] The scheme generation process uses a decision tree algorithm, and the branching conditions of the decision tree include different combinations of deposition threshold, rate threshold and acceleration threshold.

[0074] Specifically, the cleaning decision plan is generated based on the continuous monitoring results of the deposition process. The determination of cleaning priority is based on the combination of the current value of dust deposition and the deposition rate. The current value can reflect the existing accumulation on the lens surface, while the deposition rate indicates how quickly the pollution will worsen in the future. For example, if the deposition on the lens of a certain monitoring device is already large and the deposition rate is still accelerating, it means that the image clarity of the device will deteriorate quickly. In such cases, cleaning should be prioritized.

[0075] The timing of the cleanup is related to the acceleration and the arrangement of the construction plan. The acceleration indicates the trend of the deposition rate. If the rate is increasing rapidly, it means that the pollution may suddenly worsen. In this case, the cleanup needs to be done in advance. If the acceleration shows that the rate is slowing down, the cleanup can be postponed to the interval between construction to avoid interfering with the operation. For example, even if the deposition is large, it is not advisable to clean up immediately when transport vehicles are frequently entering and exiting, because it will affect the normal construction. Therefore, it should be arranged during the work interval or when the equipment is idle.

[0076] The decision tree algorithm is used to generate solutions. Its branching conditions consist of different combinations of deposition threshold, rate threshold and acceleration threshold. This setting can break down the complex judgment process into a series of clear conditional branches. When the deposition exceeds a certain level but the rate and acceleration are low, cleaning can be postponed. When both the deposition and rate exceed the threshold, the cleaning priority state is immediately entered.

[0077] It is understandable that sedimentation is essentially a dynamic accumulation process, and a single parameter cannot fully reflect the development trend of pollution. Only by combining the current state with the trend of change can we more accurately predict the availability of the lens in the future. In this way, cleaning decisions can be aligned with the actual situation on site and the construction rhythm, avoiding unnecessary repetitive operations and preventing monitoring failures caused by delayed judgments. This ensures continuous image clarity while reducing interference with the construction process.

[0078] It should be noted that the specific content of the cleaning decision plan includes the target equipment identification, the suggested cleaning time and the estimated operation time. The target equipment identification is determined by the monitoring equipment number, the suggested cleaning time is determined by coordinating the construction interval and weather conditions, and the estimated operation time is calculated based on the amount of dust accumulation and the cleaning process standards.

[0079] The cleaning decision solution is output in the form of a structured data packet, which is transmitted to the mobile cleaning terminal via a wireless communication network.

[0080] Specifically, the specific content of the cleaning decision plan first needs to clarify the target equipment identification during the formation process. This identification comes from the number assigned to the monitoring equipment during deployment. Different numbers correspond to different camera positions. Using this number, the specific object that needs to be cleaned can be accurately pointed to.

[0081] The process of determining the cleaning time takes into account both construction breaks and weather conditions. Construction breaks refer to periods when construction activities are temporarily suspended or the work density is low. Cleaning during these periods will not interfere with the construction process. Weather conditions are considered because wind and precipitation directly affect the cleaning effect. If cleaning is scheduled in windy weather, the lenses that have just been cleaned may be quickly contaminated again. Cleaning in light rain may make it easier to remove dust with the help of natural moisture. Therefore, it is necessary to comprehensively judge these factors to give a reasonable cleaning time.

[0082] The estimated operation time is calculated based on the relationship between the amount of dust deposited and the cleaning process standards. The more dust deposited, the more serious the surface pollution, and the more time it takes to completely remove it. Different cleaning processes, such as using manual wiping or spraying equipment, have different operating steps and time consumption. By comparing the degree of deposit and the process requirements, the time required to complete the cleaning can be estimated. Such time estimation helps the construction management to arrange cleaning resources in advance during scheduling.

[0083] The final cleaning decision plan is output in the form of structured data packets. This formatted data can be reliably transmitted by wireless communication devices and is easy for mobile cleaning terminals to directly parse and execute. The target device identification ensures that the cleaning objects are accurate, the suggested cleaning time ensures that the cleaning work is coordinated with the site environment and work arrangements, the estimated work duration provides a reference for manpower or equipment scheduling, and the structured transmission method ensures that the data can be shared without loss between different devices. The whole process serves to maintain the long-term clarity of the monitoring screen and the continuous effectiveness of construction supervision.

[0084] A municipal road construction decision-making system includes:

[0085] Data Acquisition Module: Collects video data through monitoring equipment deployed at the construction site, and collects wind speed and humidity data through environmental sensors;

[0086] Analysis module: Identifies construction operation types from video data, including earthwork excavation, material transportation and stockpiling operations. Based on the identified construction operation types and their spatiotemporal distribution characteristics, combined with wind speed data and humidity data, the intensity of dust generation is assessed.

[0087] Decision module: Based on the dust generation intensity and the location information of the monitoring equipment, predict the amount of dust deposited on the lenses of each monitoring device; based on the time-series change curve of the dust deposit amount, generate a cleaning decision plan for the monitoring equipment.

[0088] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art based on the actual situation. Meanwhile, other thresholds used for judgment, including but not limited to faster and slower, can be set based on the experience of those skilled in the art and are not restricted here.

[0089] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for decision-making in municipal road construction, characterized in that, Includes the following steps: Video data is collected through monitoring equipment deployed at the construction site, and wind speed and humidity data are collected through environmental sensors. The types of construction operations, including earthwork excavation, material transportation and stockpiling, are identified from video data. Based on the identified types of construction operations and their spatiotemporal distribution characteristics, combined with wind speed data and humidity data, the intensity of dust generation is assessed. Based on the dust generation intensity and the location information of the monitoring equipment, predict the amount of dust deposited on the lenses of each monitoring device; Based on the time-series variation curve of dust deposition, a cleaning decision scheme is generated for the monitoring equipment. The predicted amount of dust deposition includes: The distance attenuation component is calculated based on the straight-line distance between the monitoring equipment and the location where the dust occurs. The greater the distance, the smaller the distance attenuation component. The azimuth shielding component is calculated based on the relative azimuth angle between the monitoring equipment and the location where the dust occurs. The azimuth shielding component depends on the degree of obstruction by obstacles in the line of sight. Multiply the distance attenuation component by the azimuth shading component to obtain the spatial attenuation factor; calculate the cosine of the angle between the real-time wind speed vector and the normal direction of the monitoring equipment lens to obtain the wind direction influence factor. The effective dust intensity at the monitoring equipment location is obtained by multiplying the dust generation intensity, spatial attenuation factor, and wind direction influence factor. Based on the effective dust intensity, the amount of dust deposition is calculated using a deposition kinetic model that considers the gravitational settling and inertial collision effects of particles.

2. The municipal road construction decision-making method according to claim 1, characterized in that, Identifying construction operation types includes: Identify the movement trajectory of the bucket of excavating machinery as a characteristic of earthwork excavation operations; identify the movement path and loading / unloading behavior of transport vehicles as a characteristic of material transportation operations; identify the rate of volume change of material piles as a characteristic of stockpiling operations. The spatiotemporal distribution characteristics are characterized by recording the location, duration, and impact range of various operations. The location is determined by the latitude and longitude coordinates of the monitoring equipment, the duration is calculated by the video timestamp, and the impact range is obtained by measuring the pixel area of ​​the operation area using image recognition technology.

3. The municipal road construction decision-making method according to claim 1, characterized in that, Assessing dust generation intensity includes: The amount of dust generated by the foundation is determined according to the type of construction operation. The amount of dust generated by the foundation is based on historical measurement data and a dust release reference value is preset for each type of operation within a unit time. Wind speed and humidity data are introduced as environmental correction factors, with wind speed data used to calculate the dust diffusion rate and humidity data used to calculate the dust settling rate. A multivariate regression model was used to comprehensively calculate the basic dust generation, diffusion rate, and settling rate. The coefficients of the multivariate regression model were calibrated using on-site measured data, and the calculation result is the dust generation intensity that characterizes the overall dust situation at the construction site.

4. The municipal road construction decision-making method according to claim 1, characterized in that, The time-series variation curve is constructed using continuous time series dust deposition data, with the horizontal axis representing time and the vertical axis representing cumulative deposition. The curve characteristics of the time-series variation curves are collected. The curve characteristics include deposition rate and acceleration. The deposition rate is calculated by the change in the amount of deposition per unit time, and the acceleration is calculated by the second derivative of the deposition rate.

5. The municipal road construction decision-making method according to claim 4, characterized in that, The cleaning decision-making scheme includes determining the cleaning priority and the timing of execution. The cleaning priority is determined based on the current value of dust deposition and the deposition rate, and the timing of execution is determined based on the acceleration and construction plan. The scheme generation process uses a decision tree algorithm, and the branching conditions of the decision tree include different combinations of deposition threshold, rate threshold and acceleration threshold.

6. The municipal road construction decision-making method according to claim 5, characterized in that, The specific content of the cleaning decision plan includes the target equipment identification, the suggested cleaning time and the estimated operation time. The target equipment identification is determined by the monitoring equipment number, the suggested cleaning time is determined by coordinating the construction interval and weather conditions, and the estimated operation time is calculated based on the amount of dust accumulation and the cleaning process standards. The cleaning decision solution is output in the form of a structured data packet, which is transmitted to the mobile cleaning terminal via a wireless communication network.

7. A municipal road construction decision-making system, characterized in that, include: Data Acquisition Module: Collects video data through monitoring equipment deployed at the construction site, and collects wind speed and humidity data through environmental sensors; Analysis module: Identifies construction operation types from video data, including earthwork excavation, material transportation and stockpiling operations. Based on the identified construction operation types and their spatiotemporal distribution characteristics, combined with wind speed data and humidity data, the intensity of dust generation is assessed. Decision module: Based on the dust generation intensity and the location information of the monitoring equipment, predict the amount of dust deposited on the lenses of each monitoring device; Based on the time-series variation curve of dust deposition, a cleaning decision scheme is generated for the monitoring equipment. The predicted amount of dust deposition includes: The distance attenuation component is calculated based on the straight-line distance between the monitoring equipment and the location where the dust occurs. The greater the distance, the smaller the distance attenuation component. The azimuth shielding component is calculated based on the relative azimuth angle between the monitoring equipment and the location where the dust occurs. The azimuth shielding component depends on the degree of obstruction by obstacles in the line of sight. Multiply the distance attenuation component by the azimuth shading component to obtain the spatial attenuation factor; calculate the cosine of the angle between the real-time wind speed vector and the normal direction of the monitoring equipment lens to obtain the wind direction influence factor. The effective dust intensity at the monitoring equipment location is obtained by multiplying the dust generation intensity, spatial attenuation factor, and wind direction influence factor. Based on the effective dust intensity, the amount of dust deposition is calculated using a deposition kinetic model that considers the gravitational settling and inertial collision effects of particles.

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