Intelligent monitoring platform and method for construction dust

Abstract

The invention discloses an intelligent monitoring platform and method for construction flying dust, and relates to the technical field of flying dust monitoring data processing, and the platform comprises the steps: traversing a building construction region, laying a plurality of monitoring devices for data collection, and obtaining a plurality of construction monitoring data; determining a flying dust data association network; performing flying dust prediction on the plurality of construction monitoring data to obtain a flying dust prediction result; traversing the building construction area according to the flying dust prediction result for tracing; and optimizing the flying dust treatment scheme, and generating a flying dust treatment optimization scheme to intelligently treat flying dust in the building construction area. The technical problems of low dust pollution monitoring efficiency and insufficient treatment scheme pertinence caused by difficulty in real-time and comprehensive monitoring of a building construction area and incapability of accurately positioning a dust pollution source in the existing building construction dust monitoring treatment are solved, intelligent management of building construction dust treatment is realized, and the construction efficiency is improved. The technical effect of improving the dust pollution monitoring efficiency and the treatment pertinence of the building construction area is achieved.

Description

Technical Field

[0001] This application relates to the technical field of dust monitoring data processing, specifically to an intelligent monitoring platform and method for construction dust. Background Technology

[0002] In the context of rapid urbanization, construction has become a vital force driving urban development. However, with large-scale construction activities, dust pollution during construction has become increasingly prominent, posing a significant challenge to urban air quality, residents' health, and the ecological environment. Dust not only reduces urban visibility and increases the incidence of respiratory diseases but also causes irreversible damage to surrounding vegetation, water bodies, and other natural ecosystems. Traditional dust control methods often rely on simple experience-based judgments, which have drawbacks such as limited monitoring scope, untimely data collection, and lagging control measures. They are unable to achieve comprehensive and real-time monitoring of dust pollution sources, nor can they accurately pinpoint the main sources of dust pollution through source tracing analysis. Consequently, they fail to meet the stringent environmental protection requirements of modern construction, thus affecting the level of environmental management and green construction at construction sites.

[0003] Therefore, current dust monitoring and treatment technologies suffer from technical problems such as difficulty in real-time and comprehensive monitoring of construction areas, inability to accurately locate dust pollution sources, resulting in low efficiency of dust pollution monitoring and insufficient targeting of treatment solutions. Summary of the Invention

[0004] This application provides an intelligent monitoring platform and method for construction dust, which solves the technical problems of existing construction dust monitoring and treatment methods, such as the difficulty in real-time and comprehensive monitoring of construction areas, the inability to accurately locate dust pollution sources, and the resulting low efficiency of dust pollution monitoring and insufficient targeting of treatment solutions. It realizes intelligent management of construction dust control and achieves the technical effect of improving the efficiency of dust pollution monitoring and the targeting of treatment in construction areas.

[0005] This application provides an intelligent monitoring platform for construction dust, comprising: a construction monitoring data acquisition module, used to collect data by deploying multiple monitoring devices throughout the construction area; a dust data association network determination module, used to retrieve the locations of multiple monitoring stations of the multiple monitoring devices and perform dust association analysis on the multiple construction monitoring data to determine the dust data association network; a dust prediction result acquisition module, used to predict dust based on the dust data association network of the multiple construction monitoring data to obtain dust prediction results; a dust control scheme formulation module, used to trace the dust sources throughout the construction area based on the dust prediction results and formulate a dust control scheme based on the source tracing results; and a dust control optimization scheme generation module, used to execute the dust control scheme, utilize the multiple monitoring devices for synchronous feedback on control, and optimize the dust control scheme based on the control feedback information to generate a dust control optimization scheme for intelligent processing of dust in the construction area.

[0006] In a possible implementation, the dust data association network determination module further performs the following processing: constructing a regional coordinate system based on the construction area; identifying the coordinates of the multiple monitoring devices according to the regional coordinate system to determine the locations of multiple monitoring stations; dividing the multiple monitoring stations according to their monitoring throughput to determine multiple monitoring groups; arranging the multiple construction monitoring data according to their acquisition time sequence to obtain construction monitoring time series data; performing regression analysis on the construction monitoring time series data based on the multiple monitoring groups to generate multiple construction dust data features; performing dynamic response evaluation on the multiple construction dust data features to generate response evaluation results; and mapping the response evaluation results to the multiple construction monitoring data to construct the dust data association network.

[0007] In a possible implementation, the dust data association network determination module further performs the following processing: randomly extracting a first partition group based on the throughput optimization space, and calculating the first total throughput of the first partition group; obtaining a first neighborhood of the first partition group based on a preset neighborhood interval, wherein the first neighborhood includes multiple neighborhood monitoring station locations; sequentially calculating multiple neighborhood monitoring throughputs of the multiple neighborhood monitoring station locations; filtering the maximum value of the multiple neighborhood monitoring throughputs to obtain the optimal monitoring throughput of the first neighborhood; when the optimal monitoring throughput of the first neighborhood is greater than the first total throughput, then back-matching the first neighborhood of the optimal monitoring throughput of the first neighborhood, and partitioning based on the first neighborhood, thereby iterating to a preset iteration threshold, and outputting the multiple monitoring groups.

[0008] In a possible implementation, the dust data association network determination module further performs the following processing: performing time-domain analysis on the construction monitoring time series data based on the multiple monitoring groups to generate time-domain analysis results; identifying the dust cycle variation trend in the construction area based on the time-domain analysis results; performing frequency-domain analysis on the construction monitoring time series data based on the multiple monitoring groups to generate frequency-domain analysis results; identifying the dust spatial distribution characteristics in the construction area based on the frequency-domain analysis results; and adding the dust cycle variation trend and the dust spatial distribution characteristics to the multiple construction dust data features.

[0009] In a possible implementation, the dust control scheme formulation module further performs the following processing: traversing the construction area and combining the dust prediction results to identify dust sources and determine multiple dust source data; analyzing the spatiotemporal distribution of dust based on the multiple dust source data to generate a spatiotemporal distribution result; calculating multiple dust contribution rates based on the spatiotemporal distribution result, wherein the multiple dust contribution rates correspond to the multiple dust source data; arranging the multiple dust contribution rates in descending order to generate a dust contribution sequence; and identifying the construction area according to the dust contribution sequence to generate a source tracing result.

[0010] In a possible implementation, the dust control optimization scheme generation module further performs the following processes: executing the dust control scheme to analyze the construction area and generate regional control results; continuously monitoring the construction area using the multiple monitoring devices and generating regional monitoring results; integrating the regional control results with the regional monitoring results to generate regional dust pollution status information; determining whether the regional dust pollution status information reaches the expected dust threshold; if not, generating a feedback instruction; and optimizing the dust control scheme according to the feedback instruction to generate a dust control optimization scheme.

[0011] In a possible implementation, the dust control optimization scheme generation module further performs the following processing: if the dust pollution status information of the area does not reach the expected dust threshold, then iterates through the construction area to perform exceedance analysis and generates multiple exceedance areas; based on the multiple exceedance areas, it performs dust pollution analysis to determine multiple exceedance levels; it merges the multiple exceedance levels with the multiple exceedance areas to generate exceedance results; when there is data in the exceedance results that is greater than or equal to a preset threshold value, it generates an alarm token and adds it to the feedback instruction.

[0012] This application also provides an intelligent monitoring method for construction dust, the method comprising: deploying multiple monitoring devices throughout the construction area to collect data and obtain multiple construction monitoring data; retrieving the locations of multiple monitoring stations of the multiple monitoring devices to perform dust correlation analysis on the multiple construction monitoring data and determine a dust data correlation network; performing dust prediction on the multiple construction monitoring data according to the dust data correlation network and obtaining dust prediction results; tracing the dust sources throughout the construction area based on the dust prediction results and formulating a dust control plan based on the source tracing results; executing the dust control plan, using the multiple monitoring devices for synchronous feedback on the control, and optimizing the dust control plan in reverse based on the control feedback information to generate an optimized dust control plan for intelligent processing of dust in the construction area.

[0013] This application proposes an intelligent monitoring platform and method for construction dust. First, multiple monitoring devices are deployed throughout the construction area to collect data, obtaining various construction monitoring data. Then, based on the locations of multiple monitoring stations on these devices, the data is analyzed to determine a dust data correlation network. This network, combined with the multiple construction monitoring data, is synchronized to a dust prediction model to obtain dust prediction results. Next, the dust prediction results are used to trace the dust sources throughout the construction area, and a dust control plan is formulated based on the source tracing results. Finally, the dust control plan is implemented, and feedback from multiple monitoring devices is used to optimize the plan, generating an optimized dust control plan for intelligent processing of dust in the construction area. This solves the technical problems of existing construction dust monitoring and processing methods, such as the difficulty in real-time and comprehensive monitoring of construction areas and the inability to accurately locate dust pollution sources, leading to low monitoring efficiency and insufficient targeting of control plans. It achieves intelligent management of construction dust control, improving the efficiency of dust pollution monitoring and the targeting of control measures in construction areas. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 This is a schematic diagram of the structure of an intelligent monitoring platform for construction dust provided in an embodiment of this application; Figure 2 This is a flowchart illustrating an intelligent monitoring method for construction dust provided in an embodiment of this application.

[0016] Explanation of reference numerals in the attached diagram: Module 10 for obtaining construction monitoring data, Module 20 for determining the dust data association network, Module 30 for obtaining dust prediction results, Module 40 for formulating dust control solutions, and Module 50 for generating optimized dust control solutions. Detailed Implementation

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0020] This application provides an intelligent monitoring platform for construction dust, such as... Figure 1 As shown, the platform includes: The construction monitoring data acquisition module 10 is used to traverse the construction area and deploy multiple monitoring devices to collect data, obtaining multiple construction monitoring data sets. Deploying multiple monitoring devices in the construction area to obtain multiple construction monitoring data sets refers to using scientifically deployed monitoring stations and advanced monitoring technologies and equipment to continuously measure and record various environmental parameters generated during the construction process in real time, obtaining multiple construction monitoring data sets. Specifically, when deploying monitoring equipment, several factors need to be considered, including the selection of monitoring station locations, the type and accuracy of monitoring equipment, and the frequency and method of data acquisition, to achieve comprehensive, real-time, and accurate monitoring of dust pollution in the construction area. This may include air quality monitoring, meteorological parameter monitoring, noise monitoring, video and image monitoring, etc. Air quality monitoring data includes particulate matter concentrations (such as PM2.5, PM10, and other fine particulate matter and inhalable particulate matter) and other pollutants (such as sulfur dioxide, nitrogen oxides, and other harmful gases); meteorological parameter monitoring data includes wind speed and direction, as well as humidity and temperature, which affect the concentration and distribution of dust or particulate matter in the air; noise data is not a parameter that directly measures dust pollution, but noise during construction is often closely related to construction activities and is an indispensable part of construction monitoring; using cameras and image recognition technology, construction areas can be monitored in real time, capturing the moment and process of dust generation, providing intuitive evidence and basis for dust control.

[0021] The dust data association network determination module 20 is used to analyze multiple construction monitoring data based on the locations of multiple monitoring stations of the multiple monitoring devices to determine the dust data association network. Within the construction area, multiple monitoring stations are scientifically and rationally deployed according to the characteristics of construction activities, the location of environmentally sensitive areas, and monitoring needs. These stations are distributed in different locations to comprehensively cover the construction area and capture dust pollution in different areas. Each monitoring station is equipped with corresponding monitoring equipment that can collect various environmental parameter data during the construction process in real time and continuously. The collected raw data is then preprocessed, including data cleaning (removing outliers, missing values, etc.) and data standardization (unifying dimensions and units) to ensure data quality and consistency. Finally, statistical analysis is performed on the preprocessed monitoring data... Data analysis is conducted, including analyzing the similarities and differences between data from various monitoring stations, exploring the correlations and mutual influences between different environmental parameters, and finally, based on the results of the data analysis, a dust data correlation network is constructed. This dust data correlation network uses monitoring stations as nodes and the correlations between data between stations as edges, forming a complex network structure. Through this network, the mutual influence and transmission relationships between data from different monitoring stations, as well as the spatial distribution and dynamic changes of dust pollution in the entire construction area, can be intuitively displayed. This allows for the assessment of the importance and influence of different monitoring stations, optimization of the layout and number of monitoring stations, and improvement of monitoring efficiency and accuracy.

[0022] The dust prediction result acquisition module 30 is used to predict dust pollution from the multiple construction monitoring data according to the dust data association network, and obtain dust prediction results. The objective of the dust prediction model is clearly defined: to predict the dust concentration in a specific area over a future period (e.g., hourly, daily, weekly). Historical dust monitoring data from multiple monitoring stations is acquired. Feature extraction techniques (e.g., principal component analysis, feature reduction) are used to extract historical dust concentrations, meteorological conditions, topography, and human activity intensity from the original monitoring data to construct the prediction model. Specifically, the prediction model is built based on event sequence models and neural networks. Historical data is used to train the prediction model, and the model's predictive performance is optimized by continuously adjusting model parameters and training strategies. The trained model is evaluated using an independent test dataset, ultimately yielding a dust prediction model that predicts dust pollution in a specific area. The dust data association network, combined with multiple construction monitoring data, is then input into the constructed dust prediction model. The dust prediction model outputs dust prediction results for a future period, mainly including predicted particulate matter concentration values.

[0023] The dust control plan formulation module 40 is used to trace the dust sources across the construction area based on the dust prediction results and formulate a dust control plan accordingly. It performs a detailed analysis of the dust prediction model output, specifically analyzing key parameters such as PM10 and PM2.5 concentrations and their correlation with meteorological conditions (such as wind speed, wind direction, and humidity), to identify high-incidence areas, time periods, and potential pollution sources. Based on the prediction results, a comprehensive survey of the construction area is conducted, with a focus on the high-incidence areas identified in the prediction. During the survey, attention is paid to the actual conditions of material storage and vehicle transportation at the construction site. Then, through on-site observation and data analysis, the main sources of dust pollution are determined. Potential pollution sources include earthwork excavation, material loading and unloading, vehicle transportation, and construction machinery operation. Each potential pollution source is marked and recorded. Finally, based on the source tracing results, specific dust control objectives are defined, such as reducing specific dust levels. To address regional dust concentration and improve the construction environment, a dust pollution control plan should be developed, including source control to reduce dust generation at the source. This includes measures such as using wet methods to reduce dust during earthwork excavation, using enclosed transport vehicles to prevent material spillage during transport, and covering or sealing dust-generating materials. Process control involves taking effective measures to control dust spread during construction, such as setting up barriers and dust nets to cover exposed ground and stockpiled materials, regularly watering to reduce dust, and rationally scheduling construction time and work areas to reduce dust generation. End-of-pipe treatment addresses existing dust pollution, such as installing online dust monitoring equipment to monitor dust concentration in real time, deploying mist cannons and water trucks for dust suppression, and greening the construction area to reduce exposed ground.

[0024] The dust control optimization scheme generation module 50 is used to execute the dust control scheme, utilize multiple monitoring devices for synchronous feedback, and optimize the dust control scheme based on the feedback information to generate an optimized dust control scheme for intelligent treatment of dust in the construction area. Dust pollution control is implemented according to the established dust control scheme, utilizing multiple monitoring devices such as PM2.5 / PM10 sensors, weather stations, and video surveillance for feedback, i.e., real-time and accurate monitoring of dust data in the construction area, including real-time collection of dust concentration, diffusion direction, and dynamics of the work surface. By continuously collecting environmental data after treatment using monitoring equipment, comparing indicators such as changes in particulate matter concentration and pollution duration before and after treatment, the actual effectiveness of each treatment measure is quantitatively evaluated, and treatment feedback information is obtained. Then, based on the treatment feedback information, the treatment effect of the dust control plan is evaluated in reverse, and the dust control plan is optimized based on the evaluation results to generate an optimized dust control plan. Then, based on the optimized dust control plan, intelligent dust control in construction areas is achieved using the Internet of Things, artificial intelligence, etc. For example, dust data is monitored in real time through an intelligent monitoring platform and treatment measures are automatically adjusted; data analysis algorithms are used to predict dust pollution trends and take preventive measures in advance, thereby improving the dust control effect and environmental quality in construction areas.

[0025] An intelligent monitoring platform for construction dust according to an embodiment of the present invention addresses the technical problems of existing construction dust monitoring and treatment methods, such as the difficulty in real-time and comprehensive monitoring of construction areas, the inability to accurately locate dust pollution sources, and consequently, low monitoring efficiency and insufficient targeting of control solutions. This platform achieves intelligent management of construction dust control, thereby improving the efficiency of dust pollution monitoring and the targeting of control measures in construction areas. The intelligent monitoring platform for construction dust includes: a construction monitoring data acquisition module 10, a dust data association network determination module 20, a dust prediction result acquisition module 30, a dust control solution formulation module 40, and a dust control optimization solution generation module 50.

[0026] The specific configuration of the dust data association network determination module 20 will be described in detail below. The dust data association network determination module 20 may further include: constructing a regional coordinate system based on the construction area; identifying the coordinates of the multiple monitoring devices according to the regional coordinate system; and determining the locations of multiple monitoring stations. Based on geographic coordinates (such as latitude and longitude), a regional coordinate system is constructed in the construction area to accurately locate each position within the area. The origin, direction, and unit of the coordinate system are set according to the actual situation to ensure that it accurately reflects the layout and characteristics of the construction area. In the constructed regional coordinate system, each monitoring device is identified by coordinates to determine the locations of multiple monitoring stations, including determining the specific location of the monitoring device (such as latitude and longitude or relative coordinates), installation height, and angle. It also includes dividing the multiple monitoring stations according to their locations based on monitoring throughput to determine multiple monitoring groups. Based on the coordinates of the monitoring equipment, analyze their distribution within the construction area, including considering the coverage area, monitoring throughput, and characteristics of the construction area, to determine the appropriate locations of the monitoring stations. Based on the location, monitoring throughput, and monitoring task requirements of the monitoring stations, divide multiple monitoring stations into different monitoring groups. The monitoring stations within each monitoring group should have similar monitoring tasks and data processing needs.

[0027] This also includes arranging the multiple construction monitoring data according to the collection time sequence to obtain construction monitoring time series data. Construction monitoring data is collected from various monitoring stations according to a set collection frequency and time sequence. The collected construction monitoring data is arranged according to the collection time sequence to form construction monitoring time series data. The time series data should include information such as timestamps, monitoring station identifiers, monitoring indicators (such as PM10, PM2.5 concentrations, etc.), and monitoring values. It also includes performing regression analysis on the construction monitoring time series data based on the multiple monitoring groups to generate multiple construction dust data features. Specifically, regression analysis on the construction monitoring time series data based on multiple monitoring groups reveals the relationship between monitoring data and time, space, and other influencing factors, thereby extracting key features of construction dust data and generating multiple construction dust data features, which may include the trend of dust concentration changes, the time period of peak occurrence, and the correlation with meteorological conditions (such as wind speed and wind direction).

[0028] The method also includes dynamically evaluating the multiple construction dust data features to generate response evaluation results; mapping the response evaluation results to the multiple construction monitoring data to construct the dust data association network. The dynamic response evaluation of the generated multiple construction dust data features yields response evaluation results, including understanding the response patterns of construction dust pollution under different time and spatial conditions, and the feedback effect of monitoring data on control measures. The response evaluation results are mapped to multiple construction monitoring data to form correlations between the data. Finally, based on the mapped construction monitoring data and the response evaluation results, a dust data association network is constructed to intuitively demonstrate the interaction between construction dust pollution, monitoring data, and control measures, thereby improving the efficiency and accuracy of dust control.

[0029] The specific configuration of the dust data association network determination module 20 will be described in detail below. The dust data association network determination module 20 may further include: randomly extracting a first partition group based on the throughput optimization space, and calculating the first total throughput of the first partition group. The throughput optimization space refers to the total data processing volume composed of monitoring data from multiple monitoring stations. Within this space, different configurations or combinations may lead to different throughput performances. Randomly selecting a portion of monitoring stations from the throughput optimization space as the initial partition group means that it does not guarantee the selection of the optimal group configuration from the outset, but only provides a starting point. For the randomly extracted first partition group, the total throughput of the partition group is calculated based on the performance parameters of its internal elements (such as data transmission rate, processing capacity, etc.) and their interaction methods (such as data transmission protocol, load balancing strategy, etc.) to measure the data processing performance of the group under the current configuration. It also includes obtaining a first neighborhood of the first partition group based on a preset neighborhood interval, wherein the first neighborhood includes the locations of multiple neighboring monitoring stations. Based on the preset neighborhood interval (which may be geographically adjacent areas, data transmission delay thresholds, etc.), the locations of multiple monitoring stations adjacent to the first partition group are determined, and potential stations adjacent to the current group are determined to constitute the first neighborhood. It also includes sequentially calculating the throughput of multiple neighboring monitoring stations at their locations. The calculation of the throughput of multiple neighboring monitoring stations at their locations is described above.

[0030] This also includes filtering the maximum value of the monitored throughput of the multiple neighborhoods to obtain the optimal monitored throughput of the first neighborhood. The maximum value is selected from all the monitored throughput of the neighborhoods, i.e., the optimal monitored throughput of the first neighborhood, which is the station with the best throughput performance within that neighborhood. Furthermore, when the optimal monitored throughput of the first neighborhood is greater than the first total throughput, the first neighborhood of the optimal monitored throughput of the first neighborhood is matched in reverse, and the site is divided based on the first neighborhood. This process iterates until a preset iteration threshold is reached, and the multiple monitoring groups are output. If the optimal monitored throughput of the first neighborhood is greater than the first total throughput, it means that replacing or adding this station to the current group can improve the total throughput of the group. In this case, the station with the optimal monitored throughput of the first neighborhood is matched in reverse to its original group (if it did not originally belong to the first division group), and then a new group is reconstructed centered on this station (which may include some stations from the original group and some stations from the new neighborhood). This process is repeated until a preset iteration threshold is reached (e.g., an upper limit on the number of iterations, a throughput increase below a certain threshold, etc.), at which point the final multiple monitoring groups are output.

[0031] The specific configuration of the dust data association network determination module 20 will be described in detail below. The dust data association network determination module 20 may further include: performing time-domain analysis on the construction monitoring time series data based on the multiple monitoring groups to generate time-domain analysis results; and identifying the periodic variation trend of dust in the construction area based on the time-domain analysis results. In construction monitoring, time-domain analysis is used to analyze the changing patterns of time series data over time to reveal the periodic variation trend of dust in the construction area. Specifically, time-domain analysis methods (such as regression analysis, trend analysis, seasonal decomposition, etc.) are used to perform time-domain analysis on the construction monitoring time series data to identify the periodic and trend changes in dust concentration, generating time-domain analysis results, including a periodic variation trend chart of dust concentration, a trend prediction model, etc. Finally, the periodic variation trend of dust in the construction area is identified based on the time-domain analysis results.

[0032] It also includes performing frequency domain analysis on the construction monitoring time series data based on the multiple monitoring groups to generate frequency domain analysis results; and identifying the spatial distribution characteristics of dust in the construction area based on the frequency domain analysis results. In construction monitoring, frequency domain analysis is used to analyze the performance of time series data in the frequency domain to reveal the spatial distribution characteristics of dust in the construction area. Specifically, the time domain data is converted to the frequency domain using techniques such as Fourier transform to obtain the signal spectrum. The dominant frequency, secondary frequency, and other characteristics in the spectrum are analyzed to identify the spatial distribution characteristics of dust corresponding to different frequency components. Then, combined with the spatial location information of the monitoring groups, the spatial distribution law of dust concentration in different frequency bands is analyzed to generate frequency domain analysis results, including spectrum diagrams and dust spatial distribution characteristic maps.

[0033] This also includes adding the dust cycle variation trend and the dust spatial distribution characteristics to the multiple construction dust data features. The dust cycle variation trend and spatial distribution characteristics obtained from time-domain and frequency-domain analysis are added to the construction dust data features to more comprehensively describe and understand the dust situation in the construction area. Specifically, the time-domain analysis results (such as cycle variation trend and trend prediction model) and frequency-domain analysis results (such as spectrum diagrams and spatial distribution characteristic maps) are integrated with the original construction dust data features. Key feature parameters (such as cycle length, trend slope, dominant frequency components, etc.) are extracted from the analysis results and added to the construction dust data features.

[0034] The specific configuration of the dust control solution formulation module 40 will be described in detail below. The dust control solution formulation module 40 may further include: traversing the construction area and combining the dust prediction results to identify dust sources and determine multiple dust source data. A detailed investigation of the entire construction area is conducted, potentially including different work areas (such as excavation areas, storage areas, transportation routes, etc.) and different construction stages. Combined with the dust prediction results, the main sources causing dust are identified, such as excavation operations, construction sites, road transportation, storage yards, exposed ground, vehicle traffic, etc., forming dust source data. It also includes analyzing the spatiotemporal distribution of dust based on the multiple dust source data, generating spatiotemporal distribution results. Using the dust source data in conjunction with time (such as different time periods) and space (such as different locations), an in-depth analysis of the spatiotemporal distribution of dust is conducted. This may include assessing the dust intensity and diffusion range of different dust sources at different times and locations. Through analysis, a detailed spatiotemporal distribution of dust is generated, intuitively displaying the distribution of dust in the construction area.

[0035] It also includes calculating multiple dust contribution rates based on the spatiotemporal distribution results of the dust, and these multiple dust contribution rates correspond to the multiple dust source data. Based on the spatiotemporal distribution results of the dust, each dust source is quantitatively evaluated, considering factors such as the intensity, duration, and location of different dust sources, and its contribution rate to the overall dust level is calculated. For each identified dust source, a corresponding contribution rate is obtained, and these contribution rates have a one-to-one correspondence with the dust source data. Specifically, at each time point and spatial location, the total dust concentration of all dust sources is calculated. Assuming there are n dust sources, time t, and location (… The total dust concentration on the surface ,in, Let represent the dust concentration of the i-th dust source at time t and location (x, y). Calculate the contribution rate of each dust source to the total dust concentration, where the contribution rate of the i-th dust source at time t and location (x, y) is... The calculation formula is: .

[0036] This also includes arranging the multiple dust contribution rates in descending order to generate a dust contribution sequence. The calculated dust contribution rates are arranged in descending order, and the resulting sequence reflects the importance or influence of different dust sources on the overall dust level. Furthermore, it includes identifying the dust sources in the construction area according to the dust contribution sequence and generating source identification results. Identifying the dust sources in the construction area based on the dust contribution sequence means specially marking or labeling dust sources with high contribution rates for subsequent focused management and control. After the source identification is completed, source identification results are generated, clearly indicating the main dust sources in the construction area, their locations, contribution rates, and other information.

[0037] The specific configuration of the dust control optimization scheme generation module 50 will be described in detail below. The dust control optimization scheme generation module 50 may further include: executing the dust control scheme to analyze the construction area and generate regional control results. According to the predetermined dust control scheme (which may include measures such as water spraying for dust suppression, fencing, using low-dust building materials, and rationally arranging construction time), the dust control in the construction area is carried out. During implementation, the control effect is recorded and analyzed, for example, by measuring the concentration of particulate matter in the air and observing the dust situation in the construction area, to generate regional control results. It also includes continuously monitoring the construction area using the multiple monitoring devices and generating regional monitoring results. When implementing the control scheme, multiple monitoring devices (such as particulate matter concentration monitors, anemometers, cameras, etc.) are used to continuously and comprehensively monitor the construction area, collecting dust-related data in real time. Through data processing and analysis, regional monitoring results are generated. These results reflect the current dust pollution status of the construction area, including the degree of pollution, distribution range, and changing trends.

[0038] This also includes integrating the regional governance results with the regional monitoring results to generate regional dust pollution status information. By integrating the generated regional governance results and regional monitoring results and conducting comparative analysis, a more comprehensive understanding of the actual effectiveness of the governance plan and the current dust pollution status in the construction area can be obtained, generating regional dust pollution status information. This information not only includes comparative data before and after governance but may also include the spatiotemporal distribution characteristics of dust pollution, major pollution sources, and other information, providing a basis for subsequent decision-making. Furthermore, it includes determining whether the regional dust pollution status information reaches the expected dust pollution threshold. If not, a feedback instruction is generated, and the dust governance plan is optimized based on the feedback instruction to generate an optimized dust governance plan. Based on the integrated regional dust pollution information, it is compared with the preset dust pollution expectation threshold (i.e., the expected dust pollution control standard). If the current pollution status does not reach the dust pollution expectation threshold, it indicates that the treatment effect is not ideal or that further strengthening of treatment measures is needed. The dust pollution expectation threshold refers to the pre-set expected dust control effect. At this time, a feedback instruction is generated, pointing out the existing problems and directions for improvement. After receiving the feedback instruction, the original dust control plan will be reviewed and adjusted. Combining the actual situation and professional knowledge, the treatment plan will be optimized. The optimized plan may include adding new treatment measures, adjusting the implementation intensity or method of existing measures, increasing supervision, etc., and finally generating an optimized dust control plan.

[0039] The specific configuration of the dust control optimization scheme generation module 50 will be described in detail below. The dust control optimization scheme generation module 50 may further include: if the dust pollution status information of the area does not reach the expected dust threshold, then the construction area is traversed for exceedance analysis to generate multiple exceedance areas. If the dust pollution status information of the area does not reach the expected dust threshold, a detailed inspection and evaluation of the entire construction area is conducted, leaving no area that may generate dust pollution unchecked. Based on the regional monitoring results, the dust pollution status of each area is analyzed one by one to determine whether it exceeds the preset dust pollution standard, and areas where dust pollution exceeds the standard are marked as exceedance areas.

[0040] This also includes conducting dust pollution analysis based on the multiple areas exceeding the standards to determine multiple levels of exceedance. A thorough dust pollution analysis is performed on each area exceeding the standards, considering factors such as the intensity, duration, and spread of the pollution source. Based on the analysis results, an exceedance level is determined for each area, typically reflecting the severity of the pollution and the urgency of remediation. Different areas may have different exceedance levels, ultimately resulting in multiple exceedance levels.

[0041] This also includes merging the multiple exceedance levels with the multiple exceedance areas to generate exceedance results. When the exceedance results contain data greater than or equal to a preset threshold, an alarm token is generated and added to the feedback instruction. The exceedance level of each exceedance area is merged with its geographical location, pollution characteristics, and other information to form a complete exceedance result. This result includes not only the location and exceedance level of the exceedance area but may also include detailed information such as the specific source of pollution and its diffusion trend. When the exceedance result contains data greater than or equal to a preset threshold, an alarm token is generated and added to the feedback instruction. Specifically, the preset threshold is a threshold set according to the actual situation and governance objectives, used to determine whether the exceedance result has reached a level requiring immediate action. The generated exceedance result is checked to see if there is data greater than or equal to the preset threshold. If data meeting the condition is found, an alarm token is generated and added to the previous feedback instruction, forming a complete feedback report containing detailed exceedance information and an emergency alarm.

[0042] In the above text, refer to Figure 1 A smart monitoring platform for construction dust according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes an intelligent monitoring method for construction dust according to an embodiment of the present invention.

[0043] A smart monitoring method for construction dust, such as Figure 2 As shown, the method includes: deploying multiple monitoring devices throughout the construction area to collect data and obtain multiple construction monitoring data; retrieving the locations of multiple monitoring stations of the multiple monitoring devices to perform dust correlation analysis on the multiple construction monitoring data and determine a dust data correlation network; performing dust prediction on the multiple construction monitoring data according to the dust data correlation network and obtaining dust prediction results; tracing the dust sources throughout the construction area based on the dust prediction results and formulating a dust control plan based on the source tracing results; executing the dust control plan, using the multiple monitoring devices for synchronous feedback on the control, and optimizing the dust control plan in reverse based on the control feedback information to generate an optimized dust control plan for intelligent processing of dust in the construction area.

[0044] In one possible implementation, the analysis of multiple construction monitoring data based on the locations of multiple monitoring stations of the multiple monitoring devices to determine a dust data association network includes: constructing a regional coordinate system based on the construction area; identifying the coordinates of the multiple monitoring devices according to the regional coordinate system to determine the locations of multiple monitoring stations; dividing the multiple monitoring station locations according to monitoring throughput to determine multiple monitoring groups; arranging the multiple construction monitoring data according to the acquisition time sequence to obtain construction monitoring time series data; performing regression analysis on the construction monitoring time series data based on the multiple monitoring groups to generate multiple construction dust data features; performing dynamic response evaluation on the multiple construction dust data features to generate response evaluation results; and mapping the response evaluation results to the multiple construction monitoring data to construct the dust data association network.

[0045] In one possible implementation, multiple monitoring groups are determined by dividing the locations of the multiple monitoring stations according to their monitoring throughput. This includes: randomly extracting a first partitioned group based on the throughput optimization space and calculating the first total throughput of the first partitioned group; obtaining a first neighborhood of the first partitioned group based on a preset neighborhood interval, wherein the first neighborhood includes multiple neighborhood monitoring station locations; sequentially calculating the monitoring throughput of multiple neighborhood monitoring station locations; filtering the maximum value of the multiple neighborhood monitoring throughputs to obtain the optimal monitoring throughput of the first neighborhood; when the optimal monitoring throughput of the first neighborhood is greater than the first total throughput, then the first neighborhood of the optimal monitoring throughput of the first neighborhood is back-matched, and the group is divided based on the first neighborhood, thereby iterating until a preset iteration threshold is reached, and the multiple monitoring groups are output.

[0046] In one possible implementation, regression analysis is performed on the construction monitoring time series data based on the multiple monitoring groups to generate multiple construction dust data features, including: performing time-domain analysis on the construction monitoring time series data based on the multiple monitoring groups to generate time-domain analysis results; identifying the periodic variation trend of dust in the construction area based on the time-domain analysis results; performing frequency-domain analysis on the construction monitoring time series data based on the multiple monitoring groups to generate frequency-domain analysis results; identifying the spatial distribution characteristics of dust in the construction area based on the frequency-domain analysis results; and adding the periodic variation trend of dust and the spatial distribution characteristics of dust to the multiple construction dust data features.

[0047] In one possible implementation, tracing the dust source by traversing the construction area based on the dust prediction results includes: traversing the construction area and identifying dust sources by combining the dust prediction results to determine multiple dust source data; analyzing the spatiotemporal distribution of dust based on the multiple dust source data to generate a spatiotemporal distribution result; calculating multiple dust contribution rates based on the spatiotemporal distribution result, wherein the multiple dust contribution rates correspond to the multiple dust source data; arranging the multiple dust contribution rates in descending order to generate a dust contribution sequence; and identifying the construction area according to the dust contribution sequence to generate a source tracing result.

[0048] In one possible implementation, the dust control plan is executed, and the dust control plan is optimized by utilizing the multiple monitoring devices for feedback. This includes: executing the dust control plan to analyze the construction area and generate regional control results; continuously monitoring the construction area using the multiple monitoring devices and generating regional monitoring results; integrating the regional control results with the regional monitoring results to generate regional dust pollution status information; determining whether the regional dust pollution status information reaches the expected dust threshold; if not, generating a feedback instruction; and optimizing the dust control plan based on the feedback instruction to generate an optimized dust control plan.

[0049] In one possible implementation, determining whether the dust pollution status information of the area has reached the expected dust threshold, and if not, generating a feedback instruction, further includes: if the dust pollution status information of the area has not reached the expected dust threshold, performing exceedance analysis throughout the construction area to generate multiple exceedance areas; performing dust pollution analysis based on the multiple exceedance areas to determine multiple exceedance levels; merging the multiple exceedance levels with the multiple exceedance areas to generate exceedance results; and generating an alarm token added to the feedback instruction when there is data in the exceedance results that is greater than or equal to a preset threshold.

[0050] The intelligent monitoring platform for construction dust provided in this embodiment of the invention can execute the intelligent monitoring method for construction dust provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0051] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0052] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An intelligent monitoring platform for construction dust, characterized in that, The platform includes: The construction monitoring data acquisition module is used to traverse the construction area, deploy multiple monitoring devices to collect data, and obtain multiple construction monitoring data. The dust data association network determination module is used to retrieve the locations of multiple monitoring stations of the multiple monitoring devices to perform dust association analysis on the multiple construction monitoring data and determine the dust data association network; The dust prediction result acquisition module is used to perform dust prediction on the multiple construction monitoring data according to the dust data association network, and obtain dust prediction results; The dust control plan formulation module is used to trace the source of dust throughout the construction area based on the dust prediction results, and formulate a dust control plan based on the source tracing results. The dust control optimization scheme generation module is used to execute the dust control scheme, utilize the multiple monitoring devices for synchronous feedback on the control, and optimize the dust control scheme in reverse based on the control feedback information to generate an optimized dust control scheme for intelligent treatment of dust in the construction area.

2. The intelligent monitoring platform for construction dust as described in claim 1, characterized in that, The platform retrieves the locations of multiple monitoring stations from the multiple monitoring devices and performs dust correlation analysis on the multiple construction monitoring data to determine the dust data correlation network. The platform includes: A regional coordinate system is constructed based on the construction area, and the coordinates of the multiple monitoring devices are marked according to the regional coordinate system to determine the location of multiple monitoring stations; Based on the locations of the multiple monitoring stations and their monitoring throughput, multiple monitoring groups are determined; The multiple construction monitoring data are arranged according to the collection time sequence to obtain construction monitoring time series data; Regression analysis was performed on the construction monitoring time series data based on the multiple monitoring groups to generate multiple construction dust data features; Dynamic response evaluation is performed on the multiple construction dust data characteristics to generate response evaluation results; The response evaluation results are mapped to the multiple construction monitoring data to construct the dust data association network.

3. The intelligent monitoring platform for construction dust as described in claim 2, characterized in that, Based on the locations of the multiple monitoring stations and their monitoring throughput, multiple monitoring groups are determined, and the platform includes: The first partition group is randomly extracted based on the throughput optimization space, and the first total throughput of the first partition group is calculated. The first neighborhood of the first group is obtained based on a preset neighborhood interval, wherein the first neighborhood includes the locations of multiple neighborhood monitoring stations; The throughput of multiple neighborhood monitoring stations at each location is calculated sequentially. The maximum value of the monitoring throughput of the multiple neighborhoods is filtered to obtain the optimal monitoring throughput of the first neighborhood. When the optimal monitoring throughput of the first neighborhood is greater than the first total throughput, the first neighborhood of the optimal monitoring throughput of the first neighborhood is matched in reverse, and the first neighborhood is used for division. This process is iterated until a preset iteration threshold is reached, and the multiple monitoring groups are output.

4. The intelligent monitoring platform for construction dust as described in claim 2, characterized in that, Regression analysis is performed on the construction monitoring time series data based on the multiple monitoring groups to generate multiple construction dust data features. The platform includes: Based on the multiple monitoring groups, time-domain analysis is performed on the construction monitoring time series data to generate time-domain analysis results; Based on the time-domain analysis results, the periodic variation trend of dust in the construction area was identified; Frequency domain analysis is performed on the construction monitoring time series data based on the multiple monitoring groups to generate frequency domain analysis results; The spatial distribution characteristics of dust in the construction area are identified based on the frequency domain analysis results. The dust cycle variation trend and the dust spatial distribution characteristics are added to the multiple construction dust data features.

5. The intelligent monitoring platform for construction dust as described in claim 1, characterized in that, Based on the dust prediction results, the platform traces the dust source across the construction area. The platform includes: By traversing the construction area and combining the dust prediction results, dust sources are identified, and multiple dust source data are determined. Based on the data from the multiple dust sources, the spatiotemporal distribution of dust is analyzed, and the spatiotemporal distribution results of dust are generated. Based on the spatiotemporal distribution results of dust, multiple dust contribution rates are calculated, and these multiple dust contribution rates correspond to the multiple dust source data. The multiple dust contribution rates are sorted in descending order to generate a dust contribution sequence. The construction area is identified and traced according to the dust contribution sequence, and the traceability results are generated.

6. The intelligent monitoring platform for construction dust as described in claim 1, characterized in that, The dust control plan is implemented, and the multiple monitoring devices are used for synchronous feedback. Based on the feedback information, the dust control plan is optimized. The platform includes: The dust control plan is implemented to analyze the dust control in the construction area and generate regional control results. The construction area is continuously monitored using the aforementioned multiple monitoring devices, and regional monitoring results are generated. The regional governance results are integrated with the regional monitoring results to generate regional dust pollution status information; Determine whether the dust pollution status information of the area has reached the expected dust threshold. If not, generate a feedback instruction and optimize the dust control plan according to the feedback instruction to generate an optimized dust control plan.

7. The intelligent monitoring platform for construction dust as described in claim 6, characterized in that, The system determines whether the dust pollution status information of the area has reached the expected dust threshold. If not, it generates a feedback instruction. The platform includes: If the dust pollution status information of the area does not reach the expected dust threshold, then the construction area is traversed to perform an analysis of exceeding the standard, generating multiple areas exceeding the standard. Dust pollution analysis was conducted based on the multiple areas exceeding the standards to determine multiple levels of exceedance. The multiple exceedance levels and multiple exceedance regions are merged to generate exceedance results. When there is data in the exceedance results that is greater than or equal to a preset threshold, an alarm token is generated and added to the feedback instruction.

8. A method for intelligent monitoring of construction dust, characterized in that, The method is applied to an intelligent monitoring platform for construction dust as described in claims 1-7, and the method includes: Multiple monitoring devices were deployed throughout the construction area to collect data, resulting in a variety of construction monitoring data. By retrieving the locations of multiple monitoring stations from the multiple monitoring devices, dust correlation analysis is performed on the multiple construction monitoring data to determine the dust data correlation network. Dust prediction is performed on the multiple construction monitoring data according to the dust data association network to obtain dust prediction results; Based on the dust prediction results, the dust source is traced throughout the construction area, and a dust control plan is formulated based on the source tracing results. The dust control plan is implemented, and the multiple monitoring devices are used for synchronous feedback. Based on the feedback information, the dust control plan is optimized to generate an optimized dust control plan for intelligent treatment of dust in the construction area.

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