CN116392916B - Dust falling method and system - Google Patents
Dust falling method and system Download PDFInfo
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- CN116392916B CN116392916B CN202310682291.6A CN202310682291A CN116392916B CN 116392916 B CN116392916 B CN 116392916B CN 202310682291 A CN202310682291 A CN 202310682291A CN 116392916 B CN116392916 B CN 116392916B
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- 239000000428 dust Substances 0.000 title claims abstract description 361
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000012544 monitoring process Methods 0.000 claims abstract description 361
- 230000002159 abnormal effect Effects 0.000 claims abstract description 136
- 238000010276 construction Methods 0.000 claims abstract description 105
- 239000013618 particulate matter Substances 0.000 claims abstract description 89
- 230000007613 environmental effect Effects 0.000 claims abstract description 60
- 238000001514 detection method Methods 0.000 claims description 31
- 239000002245 particle Substances 0.000 claims description 25
- 230000002265 prevention Effects 0.000 claims description 15
- 230000001629 suppression Effects 0.000 claims description 5
- 230000000694 effects Effects 0.000 abstract description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 6
- 238000007405 data analysis Methods 0.000 description 20
- 239000007921 spray Substances 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 239000000463 material Substances 0.000 description 5
- 230000005856 abnormality Effects 0.000 description 4
- 238000009825 accumulation Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000010295 mobile communication Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 230000005250 beta ray Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
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- 239000004035 construction material Substances 0.000 description 1
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- 239000002893 slag Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D47/00—Separating dispersed particles from gases, air or vapours by liquid as separating agent
- B01D47/06—Spray cleaning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D2247/00—Details relating to the separation of dispersed particles from gases, air or vapours by liquid as separating agent
- B01D2247/08—Means for controlling the separation process
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The disclosure relates to a dust fall method and system, and relates to the field of environmental protection, wherein the method comprises the following steps: the method comprises the steps of obtaining weather information of a construction site, air quality information of the construction site, actually measured particulate matter concentration of each monitoring area, actually measured environment parameters of each monitoring area and image information of each monitoring area, determining an abnormal monitoring area, a target dust-settling device and target dust-settling parameters according to the weather information, the air quality information, the actually measured particulate matter concentration, the actually measured environment parameters and the image information, and controlling the target dust-settling device to perform dust settling on the abnormal monitoring area according to the target dust-settling parameters. Can be according to target dust fall parameter, control target dust fall device carries out the dust fall processing of pertinence to unusual monitoring area, makes dust fall device intervene in advance and carries out the dust fall to unusual monitoring area, avoids also carrying out the condition of dust fall when there is not raise dust or raise dust degree is lower to when guaranteeing dust fall efficiency and raise dust treatment effect, the water economy resource is saved, the cost of labor.
Description
Technical Field
The present disclosure relates to the field of environmental protection, and in particular, to a dust settling method and system.
Background
Dust on the ground is a pollution source which enters the atmosphere under the drive of wind power, human factors and the like, and is an important component of total suspended particles in the atmosphere. The construction site can easily generate a large amount of dust, which can seriously affect the working environment of workers and threaten the health of surrounding masses and workers.
In the related art, the dust fall device is arranged in the construction site, and the dust fall device is controlled manually to spray dust fall on the construction site. However, the opening and closing time of the dust settling device needs to be judged manually, the labor cost is too high, certain hysteresis is provided, and meanwhile, targeted dust settling treatment cannot be performed on different areas in a construction site, so that the dust settling efficiency is reduced, and the dust settling treatment effect is poor.
Disclosure of Invention
The invention aims to provide a dust falling method and system for solving the technical problems that the current dust falling device has certain hysteresis, cannot carry out targeted dust falling treatment on different areas in a construction site, is low in dust falling efficiency and is poor in dust raising treatment effect.
According to a first aspect of embodiments of the present disclosure, there is provided a dust settling method, the method comprising:
Acquiring meteorological information of a construction site, air quality information of the construction site, measured particulate matter concentration of each monitoring area in a plurality of monitoring areas included in the construction site, measured environmental parameters of each monitoring area and image information of each monitoring area; at least one dust settling device is arranged in each monitoring area;
determining an abnormal monitoring area, a target dust-settling device in the abnormal monitoring area and a target dust-settling parameter corresponding to the target dust-settling device according to the meteorological information, the air quality information, the actually measured particulate matter concentration, the actually measured environmental parameter and the image information;
and controlling the target dust falling device to fall dust in the abnormal monitoring area according to the target dust falling parameter.
Optionally, the determining the anomaly monitoring area, the target dust settling device in the anomaly monitoring area, and the target dust settling parameter corresponding to the target dust settling device according to the meteorological information, the air quality information, the actually measured particulate matter concentration, the actually measured environmental parameter, and the image information includes:
determining the dust type of the monitoring area according to the measured particulate matter concentration of the monitoring area, the measured environmental parameters of the monitoring area and the image information of the monitoring area;
For each monitoring area, determining the predicted particulate matter concentration of the monitoring area in a target time period according to the meteorological information, the air quality information, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameters of the monitoring area;
and determining the abnormal monitoring area, the target dust falling device and the target dust falling parameter according to the dust type and the predicted particulate matter concentration.
Optionally, the determining the dust type of the monitoring area according to the measured particulate matter concentration of the monitoring area, the measured environmental parameter of the monitoring area and the image information of the monitoring area includes:
image recognition is carried out on the image information of the monitoring area, and the construction type of the monitoring area is obtained;
determining the dust type of the monitoring area by utilizing a first preset corresponding relation according to the construction type of the monitoring area, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameter of the monitoring area; the first preset corresponding relation is a corresponding relation among the construction type, the actually measured particulate matter concentration, the actually measured environmental parameter and the dust type.
Optionally, the determining the predicted particulate matter concentration of the monitoring area in the target time period according to the meteorological information, the air quality information, the measured particulate matter concentration of the monitoring area and the measured environmental parameter of the monitoring area includes:
And inputting the meteorological information, the air quality information, the actually measured particulate matter concentration of the monitoring area, the actually measured environmental parameter of the monitoring area and the target time period into a concentration prediction model trained in advance, and obtaining the predicted particulate matter concentration output by the concentration prediction model.
Optionally, the determining the anomaly monitoring area, the target dust settling device and the target dust settling parameter according to the dust type and the predicted particulate matter concentration includes:
determining the abnormal monitoring area, the target dust device and the target dust parameters according to the dust type and the predicted concentration difference value corresponding to each monitoring area; the predicted concentration difference is a difference between the predicted particulate concentration of the monitoring region and a preset concentration threshold corresponding to the monitoring region.
Optionally, the determining the abnormal monitoring area, the target dust settling device and the target dust settling parameters according to the dust type and the predicted concentration difference value corresponding to each monitoring area includes:
determining the abnormal monitoring areas from a plurality of monitoring areas according to the predicted concentration difference value corresponding to each monitoring area;
And determining the target dust settling device and the target dust settling parameters according to the predicted concentration difference value corresponding to the abnormal monitoring area and the dust type of the abnormal monitoring area.
Optionally, the determining the abnormal monitoring area from the plurality of monitoring areas according to the predicted concentration difference value corresponding to each monitoring area includes:
and taking the monitoring area with the corresponding predicted concentration difference value larger than or equal to a preset value as the abnormal monitoring area in the monitoring areas.
Optionally, the abnormality monitoring area is one or more; the determining the target dust settling device and the target dust settling parameters according to the predicted concentration difference value corresponding to the abnormal monitoring area and the dust type of the abnormal monitoring area comprises the following steps:
determining the target number according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust type of the abnormal monitoring area by utilizing a second preset corresponding relation, and selecting the target number of dust falling devices in the abnormal monitoring area as target dust falling devices in the abnormal monitoring area; the second preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target quantity;
Determining target dust falling parameters corresponding to target dust falling devices in the abnormal monitoring areas by utilizing a third preset corresponding relation according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust raising type of the abnormal monitoring area; the third preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target dust fall parameter.
Optionally, the monitoring areas include a dust-raising prevention and control area, a temporary operation area and a construction operation area, the dust-raising prevention and control area is located at the edge of the construction site, and the temporary operation area and the construction operation area are located inside the dust-raising prevention and control area.
According to a second aspect of embodiments of the present disclosure, there is provided a dust fall system including a control device, a plurality of dust fall devices, a particulate matter detection device, an environment detection device, and an image acquisition device; the control device comprises an information acquisition component;
the control device is arranged in a construction site, the construction site comprises a plurality of monitoring areas, and at least one dust falling device is arranged in each monitoring area; each dust falling device is connected with the control device respectively, and the particulate matter detection device, the environment detection device and the image acquisition device are connected with the control device respectively;
The information acquisition component is used for acquiring meteorological information of the construction site and air quality information of the construction site;
the particle detection device is used for acquiring the actually measured particle concentration of each monitoring area;
the environment detection device is used for acquiring the actually measured environment parameters of each monitoring area;
the image acquisition device is used for acquiring the image information of each monitoring area;
the control device is used for determining an abnormal monitoring area, a target dust-settling device in the abnormal monitoring area and a target dust-settling parameter corresponding to the target dust-settling device according to the meteorological information, the air quality information, the actually measured particulate matter concentration, the actually measured environmental parameter and the image information, and controlling the target dust-settling device to perform dust settling on the abnormal monitoring area according to the target dust-settling parameter.
Through the technical scheme, the dust settling method provided by the embodiment of the disclosure firstly acquires weather information of a construction site, air quality information of the construction site, actually measured particulate matter concentration of each monitoring area, actually measured environmental parameters of each monitoring area and image information of each monitoring area, then determines an abnormal monitoring area, a target dust settling device in the abnormal monitoring area and target dust settling parameters corresponding to the target dust settling device according to the weather information, the air quality information, the actually measured particulate matter concentration, the actually measured environmental parameters and the image information, and controls the target dust settling device to settle dust in the abnormal monitoring area according to the target dust settling parameters. According to the method and the device, the actual dust conditions of the construction site can be judged according to the actual measured particle concentration, the actual measured environment parameters and the image information of each monitoring area and by combining with the weather information and the air quality information, so that the abnormal monitoring areas needing dust fall treatment, the target dust fall devices to be used in the abnormal monitoring areas and the target dust fall parameters of the target dust fall devices can be determined, the target dust fall devices can be controlled to carry out targeted dust fall treatment on the abnormal monitoring areas according to the target dust fall parameters, the dust fall devices can intervene in advance to carry out dust fall on the abnormal monitoring areas, dust fall is avoided when dust fall or dust fall degree is low, water resources are saved while dust fall efficiency and dust fall treatment effect are ensured, manual intervention is not needed in the dust fall process, and labor cost can be saved.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a dust suppression method according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating one step 102 according to the embodiment shown in fig. 1.
FIG. 3 is a schematic diagram of one or more monitoring areas, according to an example embodiment.
Fig. 4 is a schematic diagram illustrating a structure of a dust settling system according to an exemplary embodiment.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
FIG. 1 is a flow chart illustrating a dust suppression method according to an exemplary embodiment. As shown in fig. 1, the method includes:
step 101, acquiring meteorological information of a construction site, air quality information of the construction site, measured particulate matter concentration of each monitoring area in a plurality of monitoring areas included in the construction site, measured environmental parameters of each monitoring area and image information of each monitoring area. Wherein, be provided with at least one dust device in every monitoring area.
Step 102, determining an abnormal monitoring area, a target dust-settling device in the abnormal monitoring area and a target dust-settling parameter corresponding to the target dust-settling device according to meteorological information, air quality information, actually measured particulate matter concentration, actually measured environmental parameters and image information.
And 103, controlling the target dust falling device to fall dust in the abnormal monitoring area according to the target dust falling parameters.
For example, different areas of a construction site may generate different types of dust, which may have different characteristics, and which may have different effects on the concentration of particulate matter in the area. For example, construction dust generated in construction engineering can cause large changes in the concentration of particulate matters in a region and fluctuation is remarkable, while accumulation dust generated by open-air stacking of raw materials can cause small changes in the concentration of particulate matters in the region and fluctuation is not remarkable. Based on the method, the dust type of different areas in the construction site can be identified, future particle concentration of the different areas can be predicted, the actual dust conditions of the construction site can be judged according to the identified dust type and the predicted particle concentration, and then the dust settling device (whole or part) in the control area can be intervened in advance to carry out targeted dust settling treatment on the areas before serious dust pollution according to the judged actual dust conditions, so that dust settling efficiency and dust settling treatment effect are ensured, meanwhile, waste of water resources is avoided, whole-course automatic control is realized, manual intervention is not needed, and labor cost can be saved.
Specifically, a dust fall system composed of a control device, a plurality of dust fall devices, a particulate matter detection device, an environment detection device, and an image acquisition device may be provided in a construction site. The construction site can be divided into a plurality of monitoring areas, and at least one dust settling device is arranged in each monitoring area. Each dust falling device can be connected with the control device in a wired or wireless connection mode, and each dust falling device can fall dust on a construction site under the control of the control device. The control device may be composed of a data analysis device, a control master and at least one control slave. The data analysis device can be a cloud platform or a server, and can be remotely communicated with the control host in a 4G (English: 4th Generation Mobile Communication Technology, chinese: fourth-generation mobile communication technology), 5G (English: 5th Generation Mobile Communication Technology, chinese: fifth-generation mobile communication technology) or wired broadband mode. Each control slave can be respectively in communication connection with the control master through wireless networks such as LoRa, wifi and the like. The control master may or may not be connected to the dust-settling device (the dust-settling device connected to the control master may be referred to as a first dust-settling device), and each control slave is connected to at least one dust-settling device (the dust-settling device connected to the control slave may be referred to as a second dust-settling device). It should be noted that, using one control host to control all the dust-settling devices has complicated wiring and high performance requirement on the control host, which increases the dust-settling cost. The control host and the control slave are combined, a part of the dust falling devices are controlled by the control host, and the other part of the dust falling devices are controlled by the control slave, so that wiring can be simplified, the performance requirement on the control host is reduced, and the dust falling cost is reduced.
The particulate matter detection device may include a plurality of particulate matter detection components, the environment detection device may include a plurality of environment detection components, and the image acquisition device may include a plurality of image acquisition components. Each monitoring area may be provided with at least one particulate matter detection component, at least one environment detection component, and at least one image acquisition component. The particle detection component may include at least one of an infrared-based particle sensor, a turbidity-based particle sensor, a laser-based particle sensor, a beta-ray-based particle sensor, a particle counting-based particle sensor, a micro-oscillation balance-based particle sensor, and a weight-based particle sensor. The environment detection assembly may include at least one of a temperature and humidity sensor, a wind speed sensor, and a wind direction sensor. The image acquisition component may be a camera or an image acquisition sensor.
The control host can detect the concentration of the particulate matters in the air of each monitoring area in real time through the particulate matter detection assembly arranged in each monitoring area to obtain the actual measurement concentration of the particulate matters of each monitoring area, and detect the environmental parameters such as the temperature, the humidity, the wind speed and the wind direction of the monitoring area through the particulate matter detection assembly arranged in each monitoring area to obtain the actual measurement environmental parameters of each monitoring area so as to grasp the dust raising condition and the environmental condition of the site of each monitoring area in real time. Meanwhile, the control host can acquire images of the monitoring areas through the image acquisition assemblies arranged in the monitoring areas to obtain image information of the monitoring areas so as to provide support for judging dust type of each monitoring area. In addition, the data analysis device may be provided with an information acquisition component, and the information acquisition component may acquire weather information of the construction site and air quality information of the construction site through a network internet channel (for example, weather data of an area where the construction site is located may be acquired as weather information through a corresponding website interface, and air quality data of an area where the construction site is located may be acquired as air quality information), so as to provide data support for environmental weather condition prediction and environmental air quality prediction of the construction site. The control host can then send weather information of the construction site, air quality information of the construction site, measured particulate matter concentration of each monitoring area, measured environmental parameters of each monitoring area and image information of each monitoring area to the data analysis device.
Then, the data analysis device can judge the dust type of each monitoring area by carrying out image recognition on the image information of each monitoring area and combining the actually measured particulate matter concentration and the actually measured environmental parameter of the detection area. Meanwhile, the data analysis device can predict the particle concentration of each monitoring area according to the meteorological information and the air quality information of the construction site and the measured particle concentration and the measured environmental parameter of each monitoring area to obtain the future predicted particle concentration of each monitoring area (namely, the future predicted particle concentration of each monitoring area is predicted by combining the dust raising condition and the environmental condition of each monitoring area site and the environmental weather condition and the environmental air quality of the construction site). Then, the data analysis device can determine an abnormality monitoring area in which dust fall treatment is required, a target dust fall device to be used in the abnormality monitoring area, and target dust fall parameters of the target dust fall device according to the dust type and the predicted particulate matter concentration. Finally, the data analysis device may send the abnormal monitoring area, the target dust-settling device and the target dust-settling parameters to the control host, and the control host controls the first target dust-settling device to perform dust settling on the abnormal monitoring area according to the target dust-settling parameters corresponding to each first target dust-settling device (the first target dust-settling device is a first dust-settling device matched with the target dust-settling device), and sends the target dust-settling parameters corresponding to the second target dust-settling device to each target control slave (the target control slave is a control slave connected with the second target dust-settling device, and the second target dust-settling device is a second dust-settling device matched with the target dust-settling device), so that the target control slave controls the second target dust-settling device to perform dust settling on the abnormal monitoring area according to the target dust-settling parameters corresponding to each second target dust-settling device. The above process can be understood that after the data analysis device determines the dust settling devices needing to be turned on and the target dust settling parameters corresponding to the dust settling devices, the control host and the control slave control the dust settling devices to settle dust in the abnormal monitoring area respectively.
In summary, the dust settling method provided by the embodiment of the disclosure first obtains weather information of a construction site, air quality information of the construction site, measured particulate matter concentration of each monitoring area, measured environmental parameters of each monitoring area and image information of each monitoring area, then determines an abnormal monitoring area, a target dust settling device in the abnormal monitoring area and target dust settling parameters corresponding to the target dust settling device according to the weather information, the air quality information, the measured particulate matter concentration, the measured environmental parameters and the image information, and controls the target dust settling device to settle dust in the abnormal monitoring area according to the target dust settling parameters. According to the method and the device, the actual dust conditions of the construction site can be judged according to the actual measured particle concentration, the actual measured environment parameters and the image information of each monitoring area and by combining with the weather information and the air quality information, so that the abnormal monitoring areas needing dust fall treatment, the target dust fall devices to be used in the abnormal monitoring areas and the target dust fall parameters of the target dust fall devices can be determined, the target dust fall devices can be controlled to carry out targeted dust fall treatment on the abnormal monitoring areas according to the target dust fall parameters, the dust fall devices can intervene in advance to carry out dust fall on the abnormal monitoring areas, dust fall is avoided when dust fall or dust fall degree is low, water resources are saved while dust fall efficiency and dust fall treatment effect are ensured, manual intervention is not needed in the dust fall process, and labor cost can be saved.
Fig. 2 is a flow chart illustrating one step 102 according to the embodiment shown in fig. 1. As shown in fig. 2, step 102 may include the steps of:
step 1021, determining the dust type of each monitoring area according to the measured particulate matter concentration of the monitoring area, the measured environmental parameter of the monitoring area and the image information of the monitoring area.
For example, first, the data analysis device may perform image recognition on the image information of each monitoring area to obtain the construction type of the monitoring area. The construction type may include, among others, construction engineering, unearthing engineering, pile foundation engineering, decoration engineering, site transport (indicating the transport of construction materials, raw materials, earth, etc. into a site), material handling, material stacking, and non-construction, etc. For example, the control host may input, for each monitoring area, image information of the monitoring area into a pre-trained image recognition model to perform image recognition, so as to obtain a construction type of the monitoring area output by the image recognition model (the image recognition model substantially identifies construction behaviors included in the image information, and further determines the construction type). Then, the data analysis device can determine, for each monitoring area, a dust type of the monitoring area according to the construction type of the monitoring area, the measured particulate matter concentration of the monitoring area, and the measured environmental parameter of the monitoring area by using a first preset corresponding relation. The first preset corresponding relation is a corresponding relation among a construction type, an actually measured particulate matter concentration, an actually measured environmental parameter and a dust type. The dust types can include construction dust generated by construction engineering, soil discharging engineering, pile foundation engineering and decoration engineering, transportation dust generated by site transportation, accumulation dust generated by material stacking (coal, sand, soil, slag and building rubbish are stacked in the open air) under the action of wind force and external dust brought by other dust sources outside the construction site.
Step 1022, determining, for each monitoring area, a predicted particulate matter concentration of the monitoring area in the target time period according to the meteorological information, the air quality information, the measured particulate matter concentration of the monitoring area, and the measured environmental parameter of the monitoring area.
Specifically, the data analysis device may input weather information, air quality information, an actually measured particulate matter concentration of the monitoring area, an actually measured environmental parameter of the monitoring area, and a target time period into a concentration prediction model trained in advance, and obtain a predicted particulate matter concentration output by the concentration prediction model. The target time period may be the next time period (the duration of each time period is a preset fixed value) after the current time point, or may be a specified time period.
Step 1023, determining an abnormal monitoring area, a target dust settling device and target dust settling parameters according to the dust type and the predicted particulate matter concentration.
For example, the data analysis device may determine the abnormal monitoring area, the target dust settling device, and the target dust settling parameter according to the dust type and the predicted concentration difference value corresponding to each monitoring area. The predicted concentration difference value is a difference value between the predicted particulate matter concentration of the monitoring area and a preset concentration threshold value corresponding to the monitoring area.
For example, the data analysis device may determine an abnormal monitoring region from among the plurality of monitoring regions according to the predicted concentration difference value corresponding to each monitoring region. For example, the data analysis device may use, as the abnormality monitoring region, a monitoring region having a difference in the corresponding predicted concentration of the plurality of monitoring regions that is greater than or equal to a preset value. The difference value of the predicted concentration corresponding to a certain monitoring area is larger than or equal to a preset value, which indicates that the dust raising condition of the monitoring area in a target time period is serious, and dust falling treatment is needed to be carried out on the monitoring area in advance. Then, the data analysis device can determine the target dust-settling device and the target dust-settling parameter according to the predicted concentration difference value corresponding to the abnormal monitoring area and the dust-rising type of the abnormal monitoring area.
Specifically, when the number of the abnormal monitoring areas is one or more, the data analysis device may determine the target number according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust type of the abnormal monitoring area, and select the target number of dust settling devices in the abnormal monitoring area as the target dust settling devices in the abnormal monitoring area. The second preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target quantity. For example, when the predicted concentration difference corresponding to a certain abnormal monitoring area is large and the dust type of the abnormal monitoring area is construction dust, all dust settling devices in the abnormal monitoring area can be used as target dust settling devices in the abnormal monitoring area by utilizing the second preset corresponding relation. For another example, when the predicted concentration difference corresponding to a certain abnormal monitoring area is smaller and the dust type of the abnormal monitoring area is dust accumulation, using the second preset corresponding relation, 1/3 of the dust settling devices in the abnormal monitoring area can be used as target dust settling devices in the abnormal monitoring area. Then, the data analysis device can determine the target dust falling parameters corresponding to the target dust falling devices in the abnormal monitoring areas according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust raising type of the abnormal monitoring area by utilizing a third preset corresponding relation. The third preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target dust fall parameter. For example, when the dust fall device is a spray apparatus, the target dust fall parameters may include spray time, spray amount per minute, spray pattern (intermittent spray, continuous spray, etc.), spray angle, and the like.
In one scenario, as shown in fig. 3, the plurality of monitoring areas may include a dust prevention and control area 201, a temporary operation area 202, and a construction operation area 203, where the dust prevention and control area 201 is located at an edge of the construction site, the dust prevention and control area 201 generally has no construction behavior, the corresponding dust type is mainly external dust, and the dust prevention and control area 201 is mainly configured to prevent and control the external dust, so as to avoid the influence of the external dust on the temporary operation area 202 and the construction operation area 203. The temporary working area 202 and the construction working area 203 are located inside the dust prevention and control area 201, the construction behavior of the temporary working area 202 is less, mainly the construction behaviors such as site transportation, material loading and unloading, material stacking and the like, the corresponding dust types mainly include transportation dust and accumulation dust, the concentration change of particulate matters in the area is generally small, and fluctuation is not obvious. The construction operation area 203 is a core area in the construction site, the construction actions in the construction site are mainly concentrated in the construction operation area 203, the corresponding dust type is mainly construction dust, the concentration of the particles in the area is generally greatly changed, and the fluctuation is remarkable. It should be noted that, by dividing the construction site into the dust-raising prevention and control area 201, the temporary operation area 202 and the construction operation area 203, the dust-raising characteristics of each area can be more prominent, so that the data analysis device can more accurately judge the actual dust-raising conditions of each area, and further, the dust-lowering device can better perform dust-lowering treatment on different areas.
Further, the data analysis device can also monitor continuously in the dust falling process, and when the predicted concentration difference value corresponding to the abnormal monitoring area is smaller, a closing instruction can be issued to the control host, so that the control host controls the corresponding dust falling device to perform closing operation, and dust falling on the abnormal monitoring area is stopped.
In summary, the dust settling method provided by the embodiment of the disclosure first obtains weather information of a construction site, air quality information of the construction site, measured particulate matter concentration of each monitoring area, measured environmental parameters of each monitoring area and image information of each monitoring area, then determines an abnormal monitoring area, a target dust settling device in the abnormal monitoring area and target dust settling parameters corresponding to the target dust settling device according to the weather information, the air quality information, the measured particulate matter concentration, the measured environmental parameters and the image information, and controls the target dust settling device to settle dust in the abnormal monitoring area according to the target dust settling parameters. According to the method and the device, the actual dust conditions of the construction site can be judged according to the actual measured particle concentration, the actual measured environment parameters and the image information of each monitoring area and by combining with the weather information and the air quality information, so that the abnormal monitoring areas needing dust fall treatment, the target dust fall devices to be used in the abnormal monitoring areas and the target dust fall parameters of the target dust fall devices can be determined, the target dust fall devices can be controlled to carry out targeted dust fall treatment on the abnormal monitoring areas according to the target dust fall parameters, the dust fall devices can intervene in advance to carry out dust fall on the abnormal monitoring areas, dust fall is avoided when dust fall or dust fall degree is low, water resources are saved while dust fall efficiency and dust fall treatment effect are ensured, manual intervention is not needed in the dust fall process, and labor cost can be saved.
Fig. 4 is a schematic diagram illustrating a structure of a dust settling system according to an exemplary embodiment. As shown in fig. 4, the dust fall system 300 includes a control device 301, a plurality of dust fall devices 302, a particulate matter detection device 303, an environment detection device 304, and an image acquisition device 305. The control device 301 includes an information acquisition component 3011.
The control device 301 is arranged in a construction site comprising a plurality of monitoring areas, each of which is provided with at least one dust-settling device 302. Each of the dust settling devices 302 is connected to the control device 301, and the particulate matter detection device 303, the environment detection device 304, and the image acquisition device 305 are connected to the control device 301, respectively.
An information acquisition component 3011 for acquiring weather information of the construction site and air quality information of the construction site.
The particulate matter detection device 303 is configured to obtain a measured particulate matter concentration of each monitoring area.
The environment detection device 304 is configured to obtain an actually measured environment parameter of each monitoring area.
And the image acquisition device 305 is used for acquiring the image information of each monitoring area.
The control device 301 is configured to determine an abnormal monitoring area, a target dust-settling device in the abnormal monitoring area, and a target dust-settling parameter corresponding to the target dust-settling device according to the meteorological information, the air quality information, the actually measured particulate matter concentration, the actually measured environmental parameter, and the image information, and control the target dust-settling device to perform dust settling on the abnormal monitoring area according to the target dust-settling parameter.
Optionally, the control device 301 is configured to:
and determining the dust type of each monitoring area according to the measured particulate matter concentration of the monitoring area, the measured environmental parameters of the monitoring area and the image information of the monitoring area.
And determining the predicted particulate matter concentration of the monitoring area in the target time period according to the meteorological information, the air quality information, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameters of the monitoring area aiming at each monitoring area.
And determining an abnormal monitoring area, a target dust falling device and target dust falling parameters according to the dust type and the predicted particulate matter concentration.
Optionally, the control device 301 is configured to:
and carrying out image recognition on the image information of the monitoring area to obtain the construction type of the monitoring area.
And determining the dust type of the monitoring area by utilizing a first preset corresponding relation according to the construction type of the monitoring area, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameter of the monitoring area. The first preset corresponding relation is a corresponding relation among a construction type, an actually measured particulate matter concentration, an actually measured environmental parameter and a dust type.
Optionally, the control device 301 is configured to:
The meteorological information, the air quality information, the actually measured particulate matter concentration of the monitoring area, the actually measured environmental parameter of the monitoring area and the target time period are input into a concentration prediction model trained in advance, and the predicted particulate matter concentration output by the concentration prediction model is obtained.
Optionally, the control device 301 is configured to:
and determining an abnormal monitoring area, a target dust settling device and target dust settling parameters according to the dust type and the predicted concentration difference value corresponding to each monitoring area. The predicted concentration difference value is a difference value between the predicted particulate matter concentration of the monitoring area and a preset concentration threshold value corresponding to the monitoring area.
Optionally, the control device 301 is configured to:
and determining an abnormal monitoring area from the plurality of monitoring areas according to the predicted concentration difference value corresponding to each monitoring area.
And determining the target dust settling device and the target dust settling parameters according to the predicted concentration difference value corresponding to the abnormal monitoring area and the dust type of the abnormal monitoring area.
Optionally, the control device 301 is configured to:
and taking the monitoring area with the corresponding predicted concentration difference value larger than or equal to a preset value as an abnormal monitoring area in the plurality of monitoring areas.
Optionally, the anomaly monitoring area is one or more. The control device 301 is configured to:
And determining the target number according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust type of the abnormal monitoring area by utilizing a second preset corresponding relation, and selecting the target number of dust settling devices in the abnormal monitoring area as target dust settling devices in the abnormal monitoring area. The second preset corresponding relation is the corresponding relation among the predicted concentration difference value, the dust type and the target quantity.
And determining target dust falling parameters corresponding to the target dust falling devices in the abnormal monitoring areas by utilizing a third preset corresponding relation according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust raising type of the abnormal monitoring area. The third preset corresponding relation is the corresponding relation among the predicted concentration difference value, the dust type and the target dust fall parameter.
Optionally, the plurality of monitoring areas include dust prevention and control district, temporary operation district and construction operation district, and dust prevention and control district is located the edge in construction site, and temporary operation district and construction operation district are located dust prevention and control district inside.
In summary, the dust settling system provided in the embodiment of the present disclosure first obtains weather information of a construction site, air quality information of the construction site, measured particulate matter concentration of each monitoring area, measured environmental parameters of each monitoring area, and image information of each monitoring area, and then determines an abnormal monitoring area, a target dust settling device in the abnormal monitoring area, and target dust settling parameters corresponding to the target dust settling device according to the weather information, the air quality information, the measured particulate matter concentration, the measured environmental parameters, and the image information, and controls the target dust settling device to perform dust settling on the abnormal monitoring area according to the target dust settling parameters. According to the method and the device, the actual dust conditions of the construction site can be judged according to the actual measured particle concentration, the actual measured environment parameters and the image information of each monitoring area and by combining with the weather information and the air quality information, so that the abnormal monitoring areas needing dust fall treatment, the target dust fall devices to be used in the abnormal monitoring areas and the target dust fall parameters of the target dust fall devices can be determined, the target dust fall devices can be controlled to carry out targeted dust fall treatment on the abnormal monitoring areas according to the target dust fall parameters, the dust fall devices can intervene in advance to carry out dust fall on the abnormal monitoring areas, dust fall is avoided when dust fall or dust fall degree is low, water resources are saved while dust fall efficiency and dust fall treatment effect are ensured, manual intervention is not needed in the dust fall process, and labor cost can be saved.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.
Claims (5)
1. A method of dust fall, the method comprising:
acquiring meteorological information of a construction site, air quality information of the construction site, measured particulate matter concentration of each monitoring area in a plurality of monitoring areas included in the construction site, measured environmental parameters of each monitoring area and image information of each monitoring area; at least one dust settling device is arranged in each monitoring area;
determining an abnormal monitoring area, a target dust-settling device in the abnormal monitoring area and a target dust-settling parameter corresponding to the target dust-settling device according to the meteorological information, the air quality information, the actually measured particulate matter concentration, the actually measured environmental parameter and the image information;
Controlling the target dust falling device to fall dust in the abnormal monitoring area according to the target dust falling parameter;
the determining, according to the weather information, the air quality information, the measured particulate matter concentration, the measured environmental parameter, and the image information, an anomaly monitoring area, a target dust suppression device in the anomaly monitoring area, and a target dust suppression parameter corresponding to the target dust suppression device, includes:
determining the dust type of the monitoring area according to the measured particulate matter concentration of the monitoring area, the measured environmental parameters of the monitoring area and the image information of the monitoring area;
for each monitoring area, determining the predicted particulate matter concentration of the monitoring area in a target time period according to the meteorological information, the air quality information, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameters of the monitoring area;
determining the abnormal monitoring area, the target dust device and the target dust falling parameters according to the dust type and the predicted particulate matter concentration;
the determining the dust type of the monitoring area according to the measured particulate matter concentration of the monitoring area, the measured environmental parameter of the monitoring area and the image information of the monitoring area comprises the following steps:
Image recognition is carried out on the image information of the monitoring area, and the construction type of the monitoring area is obtained;
determining the dust type of the monitoring area by utilizing a first preset corresponding relation according to the construction type of the monitoring area, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameter of the monitoring area; the first preset corresponding relation is a corresponding relation among the construction type, the actually measured particulate matter concentration, the actually measured environmental parameter and the dust type;
the determining the anomaly monitoring area, the target dust device and the target dust parameters according to the dust type and the predicted particulate matter concentration comprises the following steps:
determining the abnormal monitoring area, the target dust device and the target dust parameters according to the dust type and the predicted concentration difference value corresponding to each monitoring area; the predicted concentration difference value is the difference value between the predicted particulate matter concentration of the monitoring area and a preset concentration threshold value corresponding to the monitoring area;
the determining the abnormal monitoring area, the target dust settling device and the target dust settling parameters according to the dust type and the predicted concentration difference value corresponding to each monitoring area comprises the following steps:
Determining the abnormal monitoring areas from a plurality of monitoring areas according to the predicted concentration difference value corresponding to each monitoring area;
determining the target dust settling device and the target dust settling parameters according to the predicted concentration difference value corresponding to the abnormal monitoring area and the dust type of the abnormal monitoring area;
the abnormal monitoring area is one or more; the determining the target dust settling device and the target dust settling parameters according to the predicted concentration difference value corresponding to the abnormal monitoring area and the dust type of the abnormal monitoring area comprises the following steps:
determining the target number according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust type of the abnormal monitoring area by utilizing a second preset corresponding relation, and selecting the target number of dust falling devices in the abnormal monitoring area as target dust falling devices in the abnormal monitoring area; the second preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target quantity;
determining target dust falling parameters corresponding to target dust falling devices in the abnormal monitoring areas by utilizing a third preset corresponding relation according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust raising type of the abnormal monitoring area; the third preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target dust fall parameter.
2. The method of claim 1, wherein determining the predicted particulate matter concentration for the monitored area over the target time period based on the weather information, the air quality information, the measured particulate matter concentration for the monitored area, and the measured environmental parameter for the monitored area comprises:
and inputting the meteorological information, the air quality information, the actually measured particulate matter concentration of the monitoring area, the actually measured environmental parameter of the monitoring area and the target time period into a concentration prediction model trained in advance, and obtaining the predicted particulate matter concentration output by the concentration prediction model.
3. The dust fall method according to claim 1, wherein the determining the abnormal monitoring area from among the plurality of monitoring areas according to the predicted concentration difference value corresponding to each of the monitoring areas includes:
and taking the monitoring area with the corresponding predicted concentration difference value larger than or equal to a preset value as the abnormal monitoring area in the monitoring areas.
4. A dust fall method according to any one of claims 1 to 3, wherein a plurality of the monitoring areas include a dust fall prevention and control area, a temporary work area and a construction work area, the dust fall prevention and control area being located at an edge of the construction site, the temporary work area and the construction work area being located inside the dust fall prevention and control area.
5. The dust fall system is characterized by comprising a control device, a plurality of dust fall devices, a particulate matter detection device, an environment detection device and an image acquisition device; the control device comprises an information acquisition component;
the control device is arranged in a construction site, the construction site comprises a plurality of monitoring areas, and at least one dust falling device is arranged in each monitoring area; each dust falling device is connected with the control device respectively, and the particulate matter detection device, the environment detection device and the image acquisition device are connected with the control device respectively;
the information acquisition component is used for acquiring meteorological information of the construction site and air quality information of the construction site;
the particle detection device is used for acquiring the actually measured particle concentration of each monitoring area;
the environment detection device is used for acquiring the actually measured environment parameters of each monitoring area;
the image acquisition device is used for acquiring the image information of each monitoring area;
the control device is used for determining an abnormal monitoring area, a target dust-settling device in the abnormal monitoring area and a target dust-settling parameter corresponding to the target dust-settling device according to the meteorological information, the air quality information, the actually measured particulate matter concentration, the actually measured environmental parameter and the image information, and controlling the target dust-settling device to settle dust in the abnormal monitoring area according to the target dust-settling parameter;
The control device is used for:
determining the dust type of the monitoring area according to the measured particulate matter concentration of the monitoring area, the measured environmental parameters of the monitoring area and the image information of the monitoring area;
for each monitoring area, determining the predicted particulate matter concentration of the monitoring area in a target time period according to the meteorological information, the air quality information, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameters of the monitoring area;
determining the abnormal monitoring area, the target dust device and the target dust falling parameters according to the dust type and the predicted particulate matter concentration;
the control device is used for:
image recognition is carried out on the image information of the monitoring area, and the construction type of the monitoring area is obtained;
determining the dust type of the monitoring area by utilizing a first preset corresponding relation according to the construction type of the monitoring area, the actually measured particulate matter concentration of the monitoring area and the actually measured environmental parameter of the monitoring area; the first preset corresponding relation is a corresponding relation among the construction type, the actually measured particulate matter concentration, the actually measured environmental parameter and the dust type;
The control device is used for:
determining the abnormal monitoring area, the target dust device and the target dust parameters according to the dust type and the predicted concentration difference value corresponding to each monitoring area; the predicted concentration difference value is the difference value between the predicted particulate matter concentration of the monitoring area and a preset concentration threshold value corresponding to the monitoring area;
the control device is used for:
determining the abnormal monitoring areas from a plurality of monitoring areas according to the predicted concentration difference value corresponding to each monitoring area;
determining the target dust settling device and the target dust settling parameters according to the predicted concentration difference value corresponding to the abnormal monitoring area and the dust type of the abnormal monitoring area;
the abnormal monitoring area is one or more; the control device is used for:
determining the target number according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust type of the abnormal monitoring area by utilizing a second preset corresponding relation, and selecting the target number of dust falling devices in the abnormal monitoring area as target dust falling devices in the abnormal monitoring area; the second preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target quantity;
Determining target dust falling parameters corresponding to target dust falling devices in the abnormal monitoring areas by utilizing a third preset corresponding relation according to the predicted concentration difference value corresponding to each abnormal monitoring area and the dust raising type of the abnormal monitoring area; the third preset corresponding relation is a corresponding relation among the predicted concentration difference value, the dust type and the target dust fall parameter.
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Denomination of invention: Dust reduction methods and systems Granted publication date: 20230901 Pledgee: Xi'an innovation financing Company limited by guarantee Pledgor: XI'AN DUOPUDUO INFORMATION TECHNOLOGY Co.,Ltd. Registration number: Y2024980009404 |