Flying dust management system for composite low-carbon construction

Abstract

The invention provides a flying dust management system for composite low-carbon construction. The flying dust management system comprises a multi-source sensing acquisition module, an edge calculation analysis module, a dynamic decision control module, a resource optimization scheduling module and a carbon emission quantitative evaluation module, the multi-source sensing acquisition module completes original data preprocessing and local calibration; the edge calculation and analysis module identifies a main pollution source and generates a concentration distribution thermodynamic diagram; the dynamic decision control module generates an optimal control strategy of the dust suppression equipment; the resource optimization scheduling module optimizes hydroelectric resource distribution, and dynamically schedules fog gun and spray tower equipment combination; and the carbon emission quantitative evaluation module calculates the net carbon emission reduction of the dust suppression measures and generates a low-carbon construction report. According to the invention, turbulence diffusion and fuzzy control are combined to realize predictive dust suppression; double-loop control of edge computing and cloud feedback is adopted, so that communication delay is reduced; and through hydroelectric constraint optimization, the carbon strength of the dust suppression system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of dust management, and in particular to a dust management system for composite low-carbon construction. Background Art

[0002] The dust management system is based on the Internet of Things and artificial intelligence technologies. It collects and transmits data monitored by sensors (PM2.5, PM10, noise, wind speed, wind direction, wind force, atmospheric pressure, air temperature and humidity, TSP, etc.) in real time, and displays the data in real time on the on-site LED screen, platform PC and mobile terminal, so that managers can remotely monitor on-site environmental data in real time and make timely decisions.

[0003] Currently, existing technologies have the following defects: in terms of monitoring: reliance on fixed national control points, insufficient deployment density (>200 meters / point); in terms of control: the timed spraying mode ignores actual working conditions, and the water and electricity waste rate reaches 40%; in terms of assessment: carbon accounting only focuses on mechanical fuel and ignores the dust suppression system's own emissions. Summary of the Invention

[0004] In order to solve the above problems, the present invention discloses a dust management system and method for composite low-carbon construction.

[0005] The specific plan is as follows:

[0006] A dust management system for composite low-carbon construction includes a multi-source sensing and acquisition module, an edge computing and analysis module, a dynamic decision-making and control module, a resource optimization and scheduling module, and a carbon emission quantification and assessment module; the multi-source sensing and acquisition module is used to collect data on dust concentration (PM2.5 / PM10), meteorological parameters (wind speed, wind direction, humidity), and mechanical operating status (position, work intensity) at the construction site in real time, and complete raw data preprocessing and local calibration; the edge computing and analysis module predicts the dust propagation path based on a physical diffusion model, identifies major pollution sources (such as excavator locations), and generates a concentration distribution heat map for the next 15 minutes; the dynamic decision-making and control module generates an optimal control strategy for dust suppression equipment (fog cannon start and stop, spray angle, power gear) based on the deviation between the predicted concentration and the target threshold; the resource optimization and scheduling module optimizes the allocation of water and electricity resources and dynamically schedules the combination of fog cannons and spray tower equipment on the premise of meeting the dust suppression effect; the carbon emission quantification and assessment module calculates the net carbon emission reduction of dust suppression measures based on the life cycle assessment (LCA) method, and generates a low-carbon construction report.

[0007] Furthermore, the multi-source sensing acquisition module sends a structured data packet to the edge computing analysis module every 5 minutes through the LoRaWAN protocol, which contains the device ID, timestamp, sensor value, and battery status fields; an adaptive transmission mechanism is used: when the PM10 concentration suddenly exceeds the threshold (such as ±20μg / m 3) immediately triggers real-time reporting (minimum delay 1.2 seconds); receives RS485 feedback signals from on-site execution equipment (such as the actual water spray volume of the fog cannon and the spray status of the tower crane) for cross-verification of sensor data;

[0008] Specifically, the adaptive weighted fusion algorithm is adopted: C_actual=(w1*C_laser+w2*C_beta) / (w1+w2), where C_laser is the laser sensor value (μg / m 3 ), C_beta is the β-ray sensor value (μg / m 3 ), w1 is the laser confidence weight (adjusted according to the humidity compensation coefficient H_c), and w2 is the β-ray confidence weight (adjusted according to the equipment wear coefficient D_w).

[0009] Furthermore, the edge computing and analysis module obtains the encrypted data stream from the perception module, decrypts it, and stores it in the edge database; receives the model parameter correction instructions (such as the adjustment value of the diffusion coefficient K) from the carbon emission assessment module through a WebSocket persistent connection; and publishes the prediction results to the dynamic decision control module through the MQTT protocol, including: the geographic coordinates (x, y) of the pollution source, the risk level (level 1-3), and the compressed concentration matrix data (10×10 grid encoded in BASE64);

[0010] Specifically, the improved particle diffusion model P(x,y,t)=Q / (4πKt)*exp(-(x-ut)^2 / (4Kt)-y^2 / (4Kt))*S_h is adopted, where Q is the pollution source intensity (g / s), K is the turbulent diffusion coefficient (m 2 / s), u is the dominant wind speed (m / s), S_h is the humidity suppression factor (range 0.6-1.2), and t is the diffusion time (s).

[0011] Furthermore, the dynamic decision control module receives the prediction data from the edge module and receives the equipment availability status table (such as water tank water level and fog cannon fault information) from the resource scheduling module; calls the resource optimization scheduling module through the RESTful API and sends JSON format control instructions;

[0012] Specifically, the fuzzy PID control algorithm is adopted: error E = set concentration - measured concentration, rate of change EC = dE / dt, control output U = KpE + Ki∫Edt + Kd*dE / dt; parameters are dynamically adjusted: Kp = f1(E,EC) (defined by the fuzzy rule table), Ki = f2(E,EC), Kd = f3(E,EC).

[0013] Furthermore, the resource optimization and scheduling module receives the HTTPPOST instruction from the analysis and decision module, obtains the equipment control requirements, and reads the real-time status of resources from the on-site PLC (via Modbus-TCP protocol): the remaining water volume in the water tank (m 3 ), available power of the substation (kW); send control frames to the execution device via the Modbus-RTU industrial bus: [device address] [function code] [power register address] [set value] [CRC]; push gRPC stream data to the carbon emission assessment module, including: actual operating time of the device (seconds), water and electricity consumption (kWh / m 3 ), execution result code (success / failure);

[0014] Specifically, the constrained particle swarm algorithm is adopted: objective function: Min(Σ(W_i*T_i)+λ*ΣWater_j); constraint conditions: PM_k≤Threshold, Water_j≤Tank_Capacity, where W_i = power of the i-th fog cannon (kW), T_i = operating time (h), λ = water-to-electricity conversion coefficient, and Water_j = water consumption of the water tank.

[0015] Furthermore, the carbon emission quantification and assessment module continuously receives gRPC data streams from the scheduling module, records the details of equipment resource consumption; obtains external factors (such as the regional power grid carbon emission factor EF_elec) from the enterprise database; sends model optimization suggestions to the edge module through the WebSocket bidirectional channel, and uploads daily reports to the management platform (HTTPS protocol), including: daily carbon emission reduction (kgCO2), water and electricity saving rate (%), and equipment utilization rate heat map;

[0016] Specifically, the LCA carbon accounting model adopted is: ΔCO2 = (E_base-E_actual)*EF_elec+Water_save*EF_water, where E_base = benchmark energy consumption (kWh), E_actual = actual energy consumption (kWh), EF_elec = regional power grid emission factor (kgCO2 / kWh), Water_save = tons of water saved, and EF_water = water treatment emission factor (kgCO2 / t).

[0017] Furthermore, the operating steps of the system are:

[0018] S1, data collection stage: the perception layer collects environmental data every 5 minutes and transmits it to the edge node through the LoRa self-organizing network;

[0019] S2, real-time analysis stage: The edge computing module performs diffusion model prediction and identifies the coordinates of the pollution source (such as the excavator operation point);

[0020] S3, decision generation stage: The dynamic decision module generates fog cannon start and stop instructions and angle adjustment plans based on the predicted concentration distribution;

[0021] S4, resource scheduling stage: the scheduling module optimizes the equipment combination (such as starting the 3#+5# fog cannon and shutting down the 7# tower crane spray);

[0022] S5, Execution feedback phase: After the actuator is activated, the perception layer verifies whether the PM10 reduction reaches the expected threshold;

[0023] S6. Carbon accounting stage: Calculate carbon emission reduction at midnight every day and generate a low-carbon construction report.

[0024] The beneficial effects of the present invention are:

[0025] 1. Combine turbulent diffusion with fuzzy control to achieve predictive dust suppression; adopt "edge computing + cloud feedback" dual-loop control to reduce communication delays; and reduce the carbon intensity of the dust suppression system itself through water and power constraint optimization.

[0026] 2. It can accurately suppress dust, shorten the dust capture response time, and achieve resource conservation, saving water and electricity through dynamic scheduling; in addition, it can also reduce carbon emissions, support docking with the BIM platform, and realize environmental mapping of the entire construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a system framework diagram of the present invention.

[0028] Figure 2 This is a diagram of the system operation steps in the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further explained below in conjunction with specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0030] As shown in the figure, the present invention provides a dust management system for composite low-carbon construction, including a multi-source sensing and acquisition module, an edge computing and analysis module, a dynamic decision-making and control module, a resource optimization and scheduling module, and a carbon emission quantification and evaluation module; the multi-source sensing and acquisition module is used to collect construction site dust concentration (PM2.5 / PM10), meteorological parameters (wind speed, wind direction, humidity), and mechanical operation status (position, work intensity) data in real time, and complete the original data preprocessing and local calibration; the edge computing and analysis module predicts the dust propagation path based on the physical diffusion model, identifies the main pollution sources (such as excavator points), and generates a concentration distribution heat map for the next 15 minutes; the dynamic decision-making and control module generates the optimal control strategy (fog cannon start and stop, spray angle, power gear) of the dust suppression equipment based on the deviation between the predicted concentration and the target threshold; the resource optimization and scheduling module optimizes the allocation of water and electricity resources and dynamically schedules the combination of fog cannon and spray tower equipment on the premise of meeting the dust suppression effect; the carbon emission quantification and evaluation module calculates the net carbon emission reduction of dust suppression measures based on the life cycle assessment (LCA) method, and generates a low-carbon construction report.

[0031] In this embodiment, the multi-source sensing acquisition module sends a structured data packet to the edge computing analysis module every 5 minutes through the LoRaWAN protocol, which includes the device ID, timestamp, sensor value, and battery status field; an adaptive transmission mechanism is adopted: when the PM10 concentration suddenly exceeds the threshold (such as ±20μg / m 3 ) immediately triggers real-time reporting (minimum delay 1.2 seconds); receives RS485 feedback signals from on-site execution equipment (such as the actual water spray volume of the fog cannon and the spray status of the tower crane) for cross-verification of sensor data;

[0032] Specifically, the adaptive weighted fusion algorithm is adopted: C_actual = (w1*C_laser+w2*C_beta) / (w1+w2), where C_laser = laser sensor value (μg / m 3 ), C_beta = β-ray sensor value (μg / m 3 ), w1 = laser confidence weight (adjusted according to humidity compensation coefficient H_c), w2 = β-ray confidence weight (adjusted according to equipment wear coefficient D_w).

[0033] In this embodiment, the edge computing and analysis module obtains the encrypted data stream from the perception module, decrypts it, and stores it in the edge database; receives the model parameter correction instructions (such as the adjustment value of the diffusion coefficient K) from the carbon emission assessment module through a WebSocket persistent connection; and publishes the prediction results to the dynamic decision control module through the MQTT protocol, including: the geographic coordinates (x, y) of the pollution source, the risk level (level 1-3), and the compressed concentration matrix data (10×10 grid encoded in BASE64);

[0034] Specifically, the improved particle diffusion model P(x,y,t)=Q / (4πKt)*exp(-(x-ut)^2 / (4Kt)-y^2 / (4Kt))*S_h is adopted, where Q=pollution source intensity (g / s), K=turbulent diffusion coefficient (m 2 / s), u = dominant wind speed (m / s), S_h = humidity suppression factor (0.6-1.2 range), t = diffusion time (s).

[0035] In this embodiment, the dynamic decision control module receives the prediction data from the edge module and receives the equipment availability status table (such as water tank water level and fog cannon fault information) from the resource scheduling module; calls the resource optimization scheduling module through the RESTful API and sends a JSON format control instruction:

[0036] {

[0037] "action_id":"CTRL_202506101030",

[0038] "target_pm10":80, / / μg / m 3

[0039] "devices":[

[0040] {"id":"cannon_07","params":{"angle":45,"power":75}},

[0041] {"id":"tower_12","params":{"duration":180}} ]

[0043] }

[0044] Specifically, the fuzzy PID control algorithm is adopted: error E = set concentration - measured concentration, rate of change EC = dE / dt, control output U = KpE + Ki∫Edt + Kd*dE / dt; parameters are dynamically adjusted: Kp = f1(E,EC) (defined by the fuzzy rule table), Ki = f2(E,EC), Kd = f3(E,EC).

[0045] In this embodiment, the resource optimization and scheduling module receives the HTTPPOST instruction from the analysis and decision module, obtains the device control requirements, and reads the real-time status of resources from the on-site PLC (via Modbus-TCP protocol): the remaining water volume in the water tank (m 3), available power of the substation (kW); send control frames to the execution device via the Modbus-RTU industrial bus: [device address] [function code] [power register address] [set value] [CRC]; push gRPC stream data to the carbon emission assessment module, including: actual operating time of the device (seconds), water and electricity consumption (kWh / m 3 ), execution result code (success / failure);

[0046] Specifically, the constrained particle swarm algorithm is adopted: objective function: Min(Σ(W_i*T_i)+λ*ΣWater_j); constraint conditions: PM_k≤Threshold, Water_j≤Tank_Capacity, where W_i = power of the i-th fog cannon (kW), T_i = operating time (h), λ = water-to-electricity conversion coefficient, and Water_j = water consumption of the water tank.

[0047] In this embodiment, the carbon emission quantification and assessment module continuously receives gRPC data streams from the scheduling module and records the details of equipment resource consumption; obtains external factors (such as the regional power grid carbon emission factor EF_elec) from the enterprise database; sends model optimization suggestions to the edge module through a WebSocket bidirectional channel, and uploads daily reports to the management platform (HTTPS protocol), including: daily carbon emission reduction (kgCO2), water and electricity saving rate (%), and equipment utilization heat map;

[0048] Specifically, the LCA carbon accounting model adopted is: ΔCO2 = (E_base-E_actual)*EF_elec+Water_save*EF_water, where E_base = benchmark energy consumption (kWh), E_actual = actual energy consumption (kWh), EF_elec = regional power grid emission factor (kgCO2 / kWh), Water_save = tons of water saved, and EF_water = water treatment emission factor (kgCO2 / t).

[0049] In this embodiment, the operating steps of the system are:

[0050] S1, data collection stage: the perception layer collects environmental data every 5 minutes and transmits it to the edge node through the LoRa self-organizing network;

[0051] S2, real-time analysis stage: The edge computing module performs diffusion model prediction and identifies the coordinates of the pollution source (such as the excavator operation point);

[0052] S3, decision generation stage: The dynamic decision module generates fog cannon start and stop instructions and angle adjustment plans based on the predicted concentration distribution;

[0053] S4, resource scheduling stage: the scheduling module optimizes the equipment combination (such as starting the 3#+5# fog cannon and shutting down the 7# tower crane spray);

[0054] S5, Execution feedback phase: After the actuator is activated, the perception layer verifies whether the PM10 reduction reaches the expected threshold;

[0055] S6. Carbon accounting stage: Calculate carbon emission reduction at midnight every day and generate a low-carbon construction report.

[0056] The technical means disclosed in the solutions of the present invention are not limited to those disclosed in the above-mentioned embodiments, but also include technical solutions composed of any combination of the above-mentioned technical features. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A dust management system for composite low-carbon construction, characterized in that: It includes a multi-source perception and acquisition module, an edge computing and analysis module, a dynamic decision-making and control module, a resource optimization and scheduling module, and a carbon emission quantification and assessment module; the multi-source perception and acquisition module is used to collect dust concentration, meteorological parameters, and mechanical operation status data at the construction site in real time, and complete the original data preprocessing and local calibration; the edge computing and analysis module predicts the dust propagation path based on the physical diffusion model, identifies the main pollution sources, and generates a concentration distribution heat map; the dynamic decision-making and control module generates the optimal control strategy for the dust suppression equipment according to the deviation between the predicted concentration and the target threshold; the resource optimization and scheduling module optimizes the allocation of water and electricity resources and dynamically schedules the combination of fog cannons and spray tower equipment on the premise of meeting the dust suppression effect; the carbon emission quantification and assessment module calculates the net carbon emission reduction of dust suppression measures based on the life cycle assessment method, and generates a low-carbon construction report.

2. A dust management system for composite low-carbon construction according to claim 1, characterized in that: The multi-source sensing acquisition module sends a structured data packet to the edge computing analysis module every 5 minutes through the LoRaWAN protocol, which contains the device ID, timestamp, sensor value, and battery status fields. It adopts an adaptive transmission mechanism: when the PM10 concentration suddenly changes and exceeds the threshold, it immediately triggers real-time reporting. It receives RS485 feedback signals from the on-site execution equipment for sensor data cross-validation. Specifically, it adopts an adaptive weighted fusion algorithm: C_actual = (w1*C_laser+w2*C_beta) / (w1+w2), where C_laser is the laser sensor value, C_beta is the beta ray sensor value, w1 is the laser confidence weight, and w2 is the beta ray confidence weight.

3. A dust management system for composite low-carbon construction according to claim 1, characterized in that: The edge computing analysis module obtains the encrypted data stream from the perception module and stores it in the edge database after decryption; receives the model parameter correction instructions from the carbon emission assessment module through the WebSocket long connection; and publishes the prediction results to the dynamic decision control module through the MQTT protocol, including: the geographic coordinates (x, y) of the pollution source, the risk level, and the concentration matrix compressed data.

4. A dust management system for composite low-carbon construction according to claim 1, characterized in that: The dynamic decision control module receives the prediction data of the edge module and receives the device availability status table from the resource scheduling module; calls the resource optimization scheduling module through the RESTful API and sends a JSON format control instruction.

5. The dust management system for composite low-carbon construction according to claim 1 is characterized in that: The resource optimization and scheduling module receives the HTTPPOST instruction from the analysis and decision-making module, obtains the device control requirements, and reads the real-time status of resources from the on-site PLC: the remaining water volume in the water tank and the available power of the substation; sends a control frame to the execution device through the Modbus-RTU industrial bus: [device address][function code][power register address][set value][CRC]; and pushes gRPC stream data to the carbon emission assessment module, including: the actual operating time of the equipment, water and electricity consumption, and the execution result code.

6. A dust management system for composite low-carbon construction according to claim 1, characterized in that: The carbon emission quantification and assessment module continuously receives gRPC data streams from the scheduling module, records the details of equipment resource consumption; obtains external factors from the enterprise database; sends model optimization suggestions to the edge module through the WebSocket bidirectional channel, and uploads daily reports to the management platform, including: carbon emission reduction, water and electricity saving rate, and equipment utilization rate heat map; Specifically, the LCA carbon accounting model adopted is: ΔCO2 = (E_base-E_actual)*EF_elec+Water_save*EF_water, where E_base is the benchmark energy consumption, E_actual is the actual energy consumption, EF_elec is the regional power grid emission factor, Water_save is the number of tons of water saved, and EF_water is the water treatment emission factor.

7. The dust management system for composite low-carbon construction according to claim 1 is characterized in that: The operating steps of the system are: S1. Data collection stage: The perception layer collects environmental data every 5 minutes and transmits it to the edge node via the LoRa self-organizing network; S2, real-time analysis stage: The edge computing module performs diffusion model predictions and identifies the coordinates of pollution sources; S3, decision-making stage: The dynamic decision module generates fog cannon start and stop instructions and angle adjustment plans based on the predicted concentration distribution; S4, resource scheduling stage: The scheduling module optimizes equipment combinations; S5. Execution feedback stage: After the actuator is activated, the sensing layer verifies whether the PM10 reduction reaches the expected threshold; S6, Carbon accounting stage: Calculate carbon emission reductions at midnight every day and generate a low-carbon construction report.

Images