CN115796423A - Method and system for relieving urban raise dust based on Internet of things monitoring - Google Patents
Method and system for relieving urban raise dust based on Internet of things monitoring Download PDFInfo
- Publication number
- CN115796423A CN115796423A CN202310070064.8A CN202310070064A CN115796423A CN 115796423 A CN115796423 A CN 115796423A CN 202310070064 A CN202310070064 A CN 202310070064A CN 115796423 A CN115796423 A CN 115796423A
- Authority
- CN
- China
- Prior art keywords
- dust
- area
- data
- monitoring
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000000428 dust Substances 0.000 title claims abstract description 284
- 238000012544 monitoring process Methods 0.000 title claims abstract description 93
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000004364 calculation method Methods 0.000 claims abstract description 17
- 238000009792 diffusion process Methods 0.000 claims abstract description 16
- 238000004891 communication Methods 0.000 claims abstract description 11
- 238000012806 monitoring device Methods 0.000 claims description 36
- 239000003016 pheromone Substances 0.000 claims description 32
- 238000010276 construction Methods 0.000 claims description 8
- 230000000116 mitigating effect Effects 0.000 claims description 8
- 230000009467 reduction Effects 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 4
- 238000012546 transfer Methods 0.000 claims description 3
- 238000011897 real-time detection Methods 0.000 abstract 1
- 238000005259 measurement Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 9
- 230000005540 biological transmission Effects 0.000 description 8
- 230000007613 environmental effect Effects 0.000 description 7
- 239000011159 matrix material Substances 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 239000004035 construction material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000002440 industrial waste Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010408 sweeping Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 239000002912 waste gas Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Abstract
The application provides a method and a system for relieving urban raise dust based on Internet of things monitoring, wherein the method comprises the following steps: acquiring data in the fixed monitoring module and the mobile monitoring module in different areas, and transmitting the data to the intelligent cloud platform in real time through the communication module; determining dust emission information; judging whether the dust information exceeds a preset threshold value; if yes, judging the dust concentration in the region matched with the dust concentration is required to be subjected to dust fall treatment; acquiring an area map, position information and meteorological data of which dust emission information exceeds a preset threshold, judging a predicted dust emission diffusion range within a certain time, determining a road network map of an area to be dusted, and weighting to generate a weighted undirected graph; calculating a dust fall route plan of the fog gun vehicle through an intelligent algorithm based on the position and road information in the weighted undirected graph; through raise dust condition in raise dust monitoring real-time detection target area, it can plan out the dust fall route of each fog gun car according to the reasonable calculation of position intelligence, and the city raise dust is effectually alleviated.
Description
Technical Field
The application relates to the technical field of urban dust fall, in particular to a method and a system for relieving urban raise dust based on Internet of things monitoring.
Background
With the increasing interest of China on air quality, the requirements of people on environment are higher and higher, in the development of humanity, it is a necessary condition to keep the living environment healthy and comfortable, in order to effectively monitor the emission situation of dust pollution in urban areas, environmental protection departments at all levels have started to adopt an online monitoring system of dust pollution sources to monitor the emission situations of dust, road dust, industrial waste gas and restaurant waste gas of related building enterprises, the running situation of environmental protection facilities and the like in real time. The dust emission and pollution emission conditions of each region are transmitted to a pollution source online monitoring system platform of an environmental protection department based on a wide area network through a fixed monitoring device, so that the environmental protection department can carry out centralized and real-time supervision on the dust emission and pollution emission conditions of each region. The existing dust fall system has a lot of bugs and fuzziness on the fixed point of vehicles and equipment, the working route and the efficiency, the monitoring of the dust is fixed point monitoring, the detection range is small, and the route in the dust fall working arrangement is long and scattered for a long time; the method and the system for relieving the urban raise dust based on the monitoring of the Internet of things are provided for solving the problems that the dust removing vehicle and the raise dust vehicle on the road go to each street of the city at fixed time and fixed point to perform dust reduction work, the efficiency of relieving the raise dust is low, the difficulty of relieving the raise dust is high, the cost of relieving the raise dust is high, the method and the system for relieving the raise dust are reasonably designed, and the method and the system for relieving the raise dust are particularly important.
Disclosure of Invention
The method and the system aim to solve the problems that an existing dust fall system is small in detection range at vehicle and equipment fixed points and working routes, and long and scattered in dust fall working arrangement for a long time; the problems of low dust management efficiency, high dust difficulty and high dust cost are solved.
In order to achieve the above purpose, the present application provides the following technical solutions:
the application provides a method for relieving urban raise dust based on Internet of things monitoring, which comprises the following steps:
acquiring data in the fixed monitoring module and the mobile monitoring module in different areas, and transmitting the data to the intelligent cloud platform in real time through the communication module;
the intelligent cloud platform determines the raise dust information according to the data in the fixed monitoring module and the mobile monitoring module;
judging whether the dust emission information exceeds a preset threshold value; if yes, judging the dust concentration in the region matched with the dust concentration is required to be subjected to dust fall treatment;
acquiring a regional map and position information of which the dust emission information exceeds a preset threshold;
acquiring meteorological data of the position of a dust raising area, and judging predicted dust raising diffusion range data within a certain time;
determining a road network map of an area to be subjected to dust reduction according to the dust emission area map, the position information and the predicted dust emission diffusion range data, and weighting the road network map and the dust emission area position to generate a weighted undirected graph;
and calculating the dust fall route plan of the fog gun vehicle through an intelligent algorithm based on the position and road information in the weighted undirected graph.
Furthermore, in the data step of the fixed monitoring module and the mobile monitoring module in different areas,
the different areas include urban roads, urban construction sites, urban restaurants and urban factories;
the fixed monitoring module comprises a fixed first dust monitoring device for monitoring urban building sites, urban restaurants and urban factories;
the method comprises the steps that mobile monitoring module data of different areas are obtained, wherein the mobile monitoring module data comprise image data obtained through an image camera module arranged on an urban sanitation vehicle and a second dust emission monitoring device;
the first raise dust monitoring device and the second raise dust monitoring device comprise a raise dust sensor for acquiring raise dust information, a wind speed sensor for acquiring wind speed data, a noise sensor for acquiring noise data, a wind direction sensor for acquiring wind direction data, a positioning module for positioning a raise dust position, and a temperature and humidity sensor for acquiring temperature and humidity data.
Further, in the step that the intelligent cloud platform confirms raise dust information according to the data in fixed monitoring module and the mobile monitoring module, include: and carrying out optimization calculation on information nodes in the first dust monitoring device and the second dust monitoring device to obtain accurate dust information.
Further, after the step of judging whether the dust information exceeds the preset threshold value, the method includes: if not, judging whether the dust raising information is continuously increased within a preset time;
if not, continuing monitoring;
if yes, the dust in the area is judged to be continuously increased, and the area is the area to be subjected to dust fall.
Further, the meteorological data of the regional position of raise dust is obtained, in judging the forecast raise dust diffusion scope data step in the certain time, include: and acquiring wind speed information and wind direction information in the dust raising area, and calculating the expected diffusion range area of the raised dust within a certain time.
Further, the step of determining a road network map of an area to be subjected to dust fall according to the dust emission area map, the position information and the predicted dust emission diffusion range data, and weighting the road network map and the dust emission area position to generate a weighted undirected graph comprises the following steps: the method comprises the steps of representing the position of each fixed monitoring module or each mobile monitoring module for determining dust emission in map information through position nodes, determining urban trunk roads connected with the position nodes in an area for determining dust emission in the map information as a road network graph of an area for dust emission and dust reduction, and giving a distance weight of an edge to the urban trunk road between the two position nodes to represent a weighted undirected graph.
Further, the step of calculating the fog gun vehicle dust fall route plan based on the position and the road information in the weighted undirected graph through an intelligent algorithm comprises the following steps:
determining a position point of the fog gun vehicle, namely a starting point, in the weighted undirected graph, and taking a position point of a region to be dusted, which is represented by the position node, as a target point;
after the number m of gun fog vehicles to be operated is confirmed, calculating the probability of the gun fog vehicles from a starting point to one of target points through an improved probability calculation formula;
and after the probability of the fog gun vehicle from the starting point to one of the target points is determined, updating the path pheromone according to an improved pheromone concentration formula, and solving the optimal solution of the total path traveled by each fog gun vehicle in the current round according to the generation times.
Further, the improved probability calculation formula is as follows:
wherein:P ij k the probability that the kth fog gun vehicle moves from the dust raising area position i to the dust raising area position j at the moment t is shown; allowed k Collecting dust raising areas of a kth gun-fog vehicle, wherein dust is not temporarily reduced; s is a certain dust raising area in the dust raising area set which is not subjected to dust fall temporarily; τ is pheromone concentration; tau is ij (t) pheromone concentration on a path from a dust raising area position i to a dust raising area position j at the moment t; d is a distance; d ij The distance from a dust raising area position i to a dust raising area position j of the fog gun vehicle is shown; alpha is pheromone weight; beta is a heuristic factor.
Further, the improved pheromone concentration formula is as follows:
wherein: q is a constant and is the total path length of the kth fog vehicle; l is k Is the path length; rho is pheromone volatilization factor.
A system for mitigating urban fugitive dust based on internet of things monitoring, the system comprising:
the fixed monitoring module and the mobile monitoring module are used for acquiring flying dust information and meteorological information of a target area;
a communication module for information transfer between system elements;
the intelligent cloud platform is used for sorting data and sending instructions;
the fog gun vehicle is used for executing a dust fall task;
and the mobile platform is used for receiving task instructions by workers and providing requirements by users.
The application provides a method and a system for relieving urban raise dust based on Internet of things monitoring, and the method and the system have the following beneficial effects:
1. the dust falling route of each fog gun vehicle can be intelligently and reasonably calculated and planned according to the position by detecting the dust flying condition in a target area in real time in the dust flying monitoring process, so that the problems that the dust flying diffusion range is enlarged, or the dust falling time of the area with more dust is short and the image dust falling effect is caused by the fact that the fog gun vehicle occupies resources in the area with smaller dust or even does not need dust falling treatment due to the working mechanism of timing and fixing points of the fog gun vehicle and the area with more dust is not treated as soon as possible are solved;
2. the data collected by each first dust monitoring device and each second dust monitoring device serving as nodes are subjected to filtering and noise reduction through optimized calculation, noise generated in the monitoring process in measurement and noise generated in the environment are eliminated, data transmission quantity is compressed, transmission of redundant data is reduced, data errors are reduced, accuracy and real-time performance of data acquisition are guaranteed, reliable information is timely provided for subsequent dust fall processing, and a system can timely control a dust fall vehicle to perform dust fall processing on the area where the dust fall monitoring system is located;
3. by taking a large number of sanitation vehicles running on urban roads as a loop of flow detection, the method can effectively increase the detection range, and can also be used for identifying whether anti-pollution measures are taken in the transportation process of construction waste transportation vehicles, construction material transportation vehicles and mud head vehicles running on the roads; the second dust emission monitoring device is used for detecting dust emission data in the running distance of the sanitation vehicle in real time and recording the position of the dust emission data when the sanitation vehicle runs; the system can timely send and sweep dust vehicles and watering lorries to clean roads, and timely send fog gun trucks to dust falling on target areas according to air raise dust.
Drawings
Fig. 1 is a schematic flow chart of a method for alleviating urban fugitive dust based on monitoring of the internet of things according to an embodiment of the present application.
Fig. 2 is a schematic block diagram of a system for mitigating urban raise dust based on monitoring of the internet of things according to an embodiment of the application.
The implementation, functional features and advantages of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a schematic flow chart of a method for mitigating urban fugitive dust based on internet of things monitoring is provided;
the method for relieving urban raise dust based on Internet of things monitoring comprises the following steps:
s1: acquiring data in the fixed monitoring module and the mobile monitoring module in different areas, and transmitting the data to the intelligent cloud platform in real time through the communication module; different areas include urban roads, urban construction sites, urban restaurants and urban factories;
the fixed monitoring module comprises a fixed first dust monitoring device for monitoring urban construction sites, urban restaurants and urban factories; the first dust monitoring device is an existing monitor used for monitoring the effects of dust, noise, wind speed, wind direction, temperature and the like in the environment, monitors the data change conditions of the dust, the noise, the wind speed, the wind direction, the temperature and the like in the environment continuously and automatically in an all-weather manner, and transmits data to the intelligent cloud platform through the wireless communication module; the method comprises the steps that mobile monitoring module data of different areas are obtained, wherein the mobile monitoring module data comprise image data obtained through an image camera module arranged on an urban sanitation vehicle and a second dust emission monitoring device; the image camera module is used for monitoring real-time image data around the environment where the vehicle is located in the urban environmental sanitation system, and is used for monitoring whether the vehicle generates a large amount of raised dust when running on an urban road through intelligent AI image processing and recognition, and can be used for timely sending dust sweeping vehicles and watering lorries to clean the road, and also can be used for recognizing whether anti-pollution measures are made in the transportation process of construction waste transportation vehicles, construction material transportation vehicles and mud head vehicles running on the road; the second dust emission monitoring device is used for detecting dust emission data in the running distance of the sanitation vehicle in real time and recording the position of the dust emission data when the sanitation vehicle runs; the acquired data is transmitted to the intelligent cloud platform through the wireless communication module; first raise dust monitoring devices and second raise dust monitoring devices all include the raise dust sensor that is used for acquireing the raise dust information, a wind speed sensor for acquireing wind speed data, a noise sensor for acquireing noise data, a wind direction sensor for acquireing wind direction data, a orientation module for fixing a position the raise dust position, a temperature and humidity sensor for acquireing temperature and humidity data, raise dust region can be set for the raise dust monitoring devices and be the scope of 500 meters of central square circle, also can further judge again according to the wind speed.
S2: the intelligent cloud platform determines the dust raising information according to the data in the fixed monitoring module and the mobile monitoring module; carrying out optimization calculation on information nodes in the first dust monitoring device and the second dust monitoring device to obtain accurate dust information;
specifically, in the monitoring data, which is transformed in discrete time, it can be understood as a linear state equation formed by using discrete time as a random variable, and the monitoring device state can be represented by a vector of an n-dimensional space, so that at time t, the monitoring device equation is: x (t) = AX (t-1) + BU (t) + W (t)
The measurement equation of the raise dust sensor is as follows: z (t) = HX (t) + V (t)
In the formula: x (t) is a state value at the time t, X (t-1) is a state value at the time t-1, Z (t) is a measured value at the time t, U (t) is a control quantity of a device at the time t, A is a state transition matrix, B is a noise matrix, H is a measurement matrix, W (t) is device noise, V (t) is measurement noise, and both W (t) and V (t) are Gaussian white noise with the mean value of zero; more specifically, the noise reduction optimization data is optimized by:
state prediction X (t | t-1) = AX (t-1|t-1) + BU (t)
Covariance prediction P (t | t-1) = AP (t-1|t-1) A' + Q
Calculating Kalman gain tg (t) = P (t | t-1) H '[ HP ((t | t-1)) H' + R ]
State update X (t | t) = X (t | t-1) + tg (t) [ Z (t) -HX (t | t-1) ]
Covariance update P (t | t) = [ I-tg (t) H ] P (t | t-1)
In the formula: x (t | t-1) is an estimated value at the time t, A' is a transposed matrix of A, Q is a covariance of system noise, tg (t) is Kalman gain, R is a covariance corresponding to measurement noise, and I is an identity matrix;
if the optimal estimated value X (t-1|t-1) at the t-1 moment and the controlled variable U (t) at the t moment have no controlled variable, the U (t) is 0; obtaining the covariance P (t | t-1) of X (t | t-1) according to the covariance P (t-1|t-1) of X (t-1|t-1); calculating to obtain Kalman gain tg (t) according to P (t | t-1) and the measurement noise covariance R; correcting the estimated value X (t | t-1) at the time t according to the measured value Z (t) to obtain the optimal estimated value X (t | t) at the time t; the data collected by each first raise dust monitoring device and each second raise dust monitoring device as nodes are filtered and denoised through optimized calculation, noise generated in measurement in the monitoring process and noise generated in the environment are eliminated, data transmission quantity is compressed, transmission of redundant data is reduced, data errors are reduced, and accuracy and real-time performance of data acquisition are guaranteed.
S3: judging whether the dust information exceeds a preset threshold value; if yes, judging the dust concentration in the region matched with the dust concentration is required to be subjected to dust fall treatment; the preset threshold value is a critical value of the dust content in the air of a health and environmental protection standard which is set manually; if the intelligent cloud platform recognizes that the obtained raise dust content value exceeds a preset threshold value, judging that the region is a region to be subjected to dust fall, and recording the position of the region;
if not, judging whether the dust raising information is continuously increased within a preset time; if the intelligent cloud platform recognizes that the obtained raise dust content value does not exceed the preset threshold value, further judging, and if not, continuing monitoring; if so, judging that the dust in the area is continuously increased, wherein the area is an area to be subjected to dust fall, and the area to be subjected to dust fall is an area needing dust fall for the device; if the dust content is gradually increased, the position where the dust content is gradually increased is identified and is also treated as the region to be dusted.
S4: acquiring an area map and position information of which the dust emission information exceeds a preset threshold; specifically, the position marked in the step 3 is displayed in a map, and the map is divided into areas; the areas to be dusted are further displayed, and the areas which are not the target areas are hidden in the map.
S5: acquiring meteorological data of a dust raising area position, and judging predicted dust raising diffusion range data within a certain time; acquiring wind speed information and wind direction information in a dust raising area, and calculating a predicted dust spreading range area within a certain time; specifically, the position to which the dust is diffused can be judged according to the content and the direction of the dust, the diffusion speed of the dust in a time period is further judged according to the wind speed, the position of the dust in the map is extended along with the direction of the wind direction, the extending distance of the dust is matched according to historical storage data, and meanwhile the approximate direction of the dust position can be deduced.
S6: determining a road network map of an area to be subjected to dust fall according to the dust emission area map, the position information and the predicted dust emission diffusion range data, and weighting the road network map and the dust emission area position to generate a weighted undirected graph; representing the position in each fixed monitoring module or mobile monitoring module for determining dust emission in map information through position nodes, determining urban trunk roads connected with the position nodes in the area for determining dust emission in the map information as a road network map of the dust emission area to be reduced, and giving a distance weight of an edge to the urban trunk road between the two position nodes to represent a weighted undirected graph; the weight does not necessarily represent the distance, and can be expressed in a diversified manner as data related to the cost; the weighted undirected graph is a graph model in which each edge is associated with a weight value or a cost; such a diagram can naturally represent many applications; in the road network diagram, the edge represents a route, and the weight value can represent distance, time or cost; according to the vehicle path distance and the running speed of the fog gun vehicle when the fog gun vehicle works in the map information, the distance of the dust fall of each fog gun vehicle can be calculated according to the calculation of 7-9 hours of work of each vehicle in one day, and the speed of the fog gun vehicle can be reduced by 25% when the fog gun vehicle runs in an area with more dust.
S7: calculating a dust fall route plan of the fog gun vehicle through an intelligent algorithm based on the position and road information in the weighted undirected graph;
determining a position point of the fog gun vehicle, namely a starting point, in the weighted undirected graph, and taking a position point of a region to be dusted, which is represented by the position node, as a target point; after the number m of gun fog vehicles to be operated is confirmed, calculating the probability of the gun fog vehicles from a starting point to one of target points through an improved probability calculation formula; after the probability of the fog gun vehicles from the starting point to one of the target points is determined, updating the path pheromone according to an improved pheromone concentration formula, and solving the optimal solution of the total path traveled by each fog gun vehicle in the current round according to the number of generations, namely subtracting 1 from the total number of connecting paths between one target point and the other target point; when the dust monitoring detects that the dust exists in the target area in real time, the dust-fall path of each fog gun vehicle can be reasonably calculated according to the position, the fog gun vehicles are prevented from working at fixed time and fixed point, resources are occupied in the areas with smaller dust and even areas without dust-fall treatment, the areas with more dust are not treated as soon as possible, the dust diffusion range is enlarged, or the areas with more dust fall time is short, and the image dust-fall effect is achieved;
when the vehicle reaches the other dustfall position from one dustfall position, the probability of selecting one of the roads in the dustfall area is calculated;
the improved probability calculation formula is as follows:
wherein:P ij k the kth gun fog vehicle raises dust at the moment tProbability that the area position i moves to the dust raising area position j; allowed k Collecting dust raising areas of a kth gun fog vehicle which are not subjected to dust fall temporarily; s is a certain dust raising area in the dust raising area set which is not subjected to dust fall temporarily; τ is pheromone concentration; tau is ij (t) pheromone concentration on a path from a dust raising area position i to a dust raising area position j at the moment t; d is a distance; d ij The distance from a dust raising area position i to a dust raising area position j of the fog gun vehicle is shown; alpha is pheromone weight, namely the importance degree of the information quantity accumulated by the vehicle in the moving process to the vehicle for selecting the path, and the larger the weight is, the higher the probability of the path selected by the vehicle to walk is, and the lower the randomness of the selection is; beta is a heuristic factor, namely the heuristic function factor reflects the relative importance degree of heuristic information in the process of guiding vehicle selection; when the pheromone concentration is the same, the smaller the route from the area A to the area B, the higher the probability of selecting the route, which is equivalent to that one person looks down on a map route, can see a plurality of routes at the same time, and the probability of selecting a shorter route is higher;
in the vehicle selection process, the pheromone generated each time is changed, and the pheromone concentration at the t +1 moment is related to the original pheromone concentration at the t moment and also related to the new pheromone concentration left by the vehicle which passes through the route at the t moment; the improved pheromone concentration formula is as follows:
wherein: q is a constant and is the total path length of the kth fog vehicle; l is k Is the path length; rho is pheromone volatilization factor;
τ ij (t) is the pheromone concentration of the path from city i to city j at time t, then τ ij (t + 1) is the concentration at the next round; when a new iteration is performed in the new algorithm at the time t +1, the new iteration is influenced by the pheromone concentration, namely the summation item, newly left by the previous vehicle at the time t; leading the pheromone concentration of the path to be further increased on the basis of the original value; the minimum value of the total path traveled by each vehicle in the current round is obtained and is compared with the optimal solution of the previous roundThen, the minimum is recorded as the current optimal solution; then judging whether the solution is the optimal solution, and if the maximum iteration times are reached, wherein the maximum number of selected lost generations can be set artificially; stopping the algorithm and outputting the current optimal solution; if not, clearing the path which is recorded by the current wheel and is traveled by the vehicle, and returning to the probability calculation for recalculation; after probability calculation, performing pheromone updating calculation to judge whether the optimal solution is the optimal solution or not, namely one iteration, wherein in each iteration, a plurality of vehicles are randomly selected at a certain region position point as a starting point; when the vehicle selects the next region position point, the vehicle is determined according to an improved probability formula, and the improved probability formula is influenced by the pheromone concentration recorded in the previous iteration, so that the pheromone concentration needs to be updated after each iteration;
the method comprises the steps of monitoring urban environments in real time according to monitoring modules, carrying out filtering and noise reduction processing on data collected by each first dust monitoring device and each second dust monitoring device as nodes through optimized calculation when dust data acquired by a plurality of monitoring modules are transmitted to an intelligent cloud platform, eliminating noise generated in the monitoring process in measurement and noise generated by the environment, compressing data transmission quantity, reducing transmission of redundant data, reducing data errors and ensuring accuracy and real-time performance of data acquisition; when different monitoring devices detect that the dust content in the air in the area where the intelligent cloud platform is located exceeds a preset value, the intelligent cloud platform can timely and pertinently generate a fog gun vehicle route plan, so that the fog gun vehicle can pertinently and reasonably perform dust fall treatment on each target area.
Referring to fig. 2, a schematic block diagram of a system for mitigating urban raise dust based on internet of things monitoring is shown;
the utility model provides a system for alleviate city raise dust based on thing networking monitoring includes:
the fixed monitoring module and the mobile monitoring module are used for acquiring flying dust information and meteorological information of a target area; the dust information data can be data related to dust; for example, the fugitive dust data may include, but is not limited to, fugitive dust height, particulate concentration in the air, fugitive dust duration, visibility of fugitive dust area, etc., and the meteorological information data thereof includes, but is not limited to, rainfall data, air humidity, etc.;
a communication module for information transfer between system elements; the communication module is an existing element with network communication;
the intelligent cloud platform is used for sorting data and sending instructions; the intelligent cloud platform sends a task instruction to a mobile end of a worker after intelligently generating a dust-settling route, and the worker settles dust in each area of a city according to the task instruction;
the fog gun vehicle is used for executing a dust fall task; the fog gun vehicle drops dust in the city through the water spray fog;
the mobile platform is used for receiving task instructions and providing requirements for the user by the staff, and can be an APP (application) matched with a mobile phone;
specifically, when data collected by each first dust monitoring device and each second dust monitoring device serving as nodes are transmitted to the intelligent cloud platform, filtering and denoising are performed, noise generated in measurement in a monitoring process and noise generated in the environment are eliminated, data transmission quantity is compressed, transmission of redundant data is reduced, data errors are reduced, and accuracy and real-time performance of data collection are guaranteed; when different monitoring devices detect that the dust content in the air in the areas where the monitoring devices are located exceeds a preset value, the intelligent cloud platform can timely and specifically generate fog gun vehicle route planning, and sends a task instruction to the moving end of a matched worker, so that the fog gun vehicle can specifically and reasonably perform dust fall processing on each target area; simultaneously the resident user can also know the environmental data and the dust fall condition in each region through mobile platform, and it can appraise it after registering the account number by real name, also can report through APP when the discovery has a certain region to produce a large amount of raise dusts because of construction or fitment, also can load its reservation special vehicle through APP when having the needs to clear up construction rubbish to reduce and load the produced raise dusts of denormal.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, apparatus, article, or method that comprises the element.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Although embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A method for relieving urban raise dust based on Internet of things monitoring is characterized by comprising the following steps:
acquiring data in the fixed monitoring module and the mobile monitoring module in different areas, and transmitting the data to the intelligent cloud platform in real time through the communication module;
the intelligent cloud platform determines the dust raising information according to the data in the fixed monitoring module and the mobile monitoring module;
judging whether the dust information exceeds a preset threshold value; if yes, judging the concentration of the raised dust in the region matched with the area as needing to be subjected to dust fall treatment;
acquiring an area map and position information of which the dust emission information exceeds a preset threshold;
acquiring meteorological data of a dust raising area position, and judging predicted dust raising diffusion range data within a certain time;
determining a road network map of an area to be subjected to dust fall according to the dust emission area map, the position information and the predicted dust emission diffusion range data, and weighting the road network map and the dust emission area position to generate a weighted undirected graph;
and calculating the dust fall route plan of the fog gun vehicle by an intelligent algorithm based on the position and road information in the weighted undirected graph.
2. The method for alleviating urban fugitive dust based on monitoring of the internet of things according to claim 1, wherein in the step of data in the fixed monitoring module and the mobile monitoring module in different areas,
the different areas include urban roads, urban construction sites, urban restaurants and urban factories;
the fixed monitoring module comprises a fixed first dust monitoring device for monitoring urban building sites, urban restaurants and urban factories;
the method comprises the steps that mobile monitoring module data of different areas are obtained, wherein the mobile monitoring module data comprise image data obtained through an image camera module arranged on an urban sanitation vehicle and a second dust emission monitoring device;
first raise dust monitoring devices and second raise dust monitoring devices all include the raise dust sensor that is used for acquireing raise dust information, the air velocity transducer that is used for acquireing wind speed data, the noise sensor that is used for acquireing noise data, the wind direction sensor that is used for acquireing wind direction data, the orientation module that is used for fixing a position the raise dust position, the temperature and humidity sensor that is used for acquireing temperature and humidity data.
3. The method for alleviating urban raise dust based on monitoring of the internet of things according to claim 2, wherein the step of determining the raise dust information by the smart cloud platform according to the data in the fixed monitoring module and the mobile monitoring module comprises the following steps: and carrying out optimization calculation on information nodes in the first dust monitoring device and the second dust monitoring device to obtain accurate dust information.
4. The method for relieving urban raise dust based on monitoring of the internet of things according to claim 1, wherein the step of judging whether the raise dust information exceeds a preset threshold value comprises the following steps: if not, judging whether the dust raising information is continuously increased within a preset time;
if not, continuing monitoring;
if yes, the dust in the area is judged to be continuously increased, and the area is the area to be subjected to dust fall.
5. The method for monitoring and relieving urban raise dust based on the Internet of things according to claim 1, wherein the step of obtaining meteorological data of the position of a raise dust area and judging the data of the expected raise dust diffusion range within a certain time comprises the following steps: and acquiring wind speed information and wind direction information in the dust raising area, and calculating the expected diffusion range area of the raised dust within a certain time.
6. The method for relieving urban raise dust based on monitoring of the Internet of things according to claim 1, wherein in the step of determining a road network map of an area to be subjected to dust reduction according to a raise dust area map, position information and expected raise dust diffusion range data, and weighting the road network map and the raise dust area position to generate a weighted undirected graph, the method comprises the following steps of: the method comprises the steps of representing the position in each fixed monitoring module or mobile monitoring module for determining the dust emission in map information through position nodes, determining urban trunk roads connected with the position nodes in the area for determining the dust emission in the map information as a road network graph of the dust emission area to be reduced, and giving edge distance weight to the urban trunk roads between the two position nodes to represent a weighted undirected graph.
7. The method for alleviating urban fugitive dust based on monitoring of the internet of things according to claim 6, wherein the step of calculating the fog gun vehicle dustfall route plan by an intelligent algorithm based on the position and road information in the weighted undirected graph comprises:
determining a position point of the fog gun vehicle, namely a starting point, in the weighted undirected graph, and taking a position point of a region to be dusted, which is represented by the position node, as a target point;
after the number m of gun fog vehicles to be operated is confirmed, calculating the probability of the gun fog vehicles from a starting point to one of target points through an improved probability calculation formula;
and after the probability of the fog gun vehicle from the starting point to one of the target points is determined, updating the path pheromone according to an improved pheromone concentration formula, and solving the optimal solution of the total path traveled by each fog gun vehicle in the current round according to the generation times.
8. The method for mitigating urban fugitive dust based on monitoring of the internet of things according to claim 7, wherein the improvement probability calculation formula is:
wherein:P ij k the probability that the kth fog gun vehicle moves from the dust raising area position i to the dust raising area position j at the moment t is shown; allowed k Collecting dust raising areas of a kth gun fog vehicle which are not subjected to dust fall temporarily; s is a certain dust raising area in the dust raising area set which is not subjected to dust fall temporarily; τ is pheromone concentration; tau is ij (t) pheromone concentration on a path from a dust raising area position i to a dust raising area position j at the moment t; d is a distance; d is a radical of ij The distance from a dust raising area position i to a dust raising area position j of the fog gun vehicle is shown; alpha is pheromone weight; beta is a heuristic factor.
9. The method for mitigating urban fugitive dust based on monitoring of the internet of things according to claim 7, wherein the modified pheromone concentration formula is:
wherein: q is a constant and is the total path length of the kth fog vehicle; l is k Is the path length; rho is pheromone volatilization factor.
10. A system for mitigating urban fugitive dust based on internet of things monitoring, the system being configured to implement the method for mitigating urban fugitive dust based on internet of things monitoring as claimed in any one of claims 1 to 9, the system comprising:
the fixed monitoring module and the mobile monitoring module are used for acquiring flying dust information and meteorological information of a target area;
a communication module for information transfer between system elements;
the intelligent cloud platform is used for sorting data and sending instructions;
the fog gun vehicle is used for executing a dust fall task;
and the mobile platform is used for receiving task instructions by workers and providing requirements by users.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310070064.8A CN115796423A (en) | 2023-02-07 | 2023-02-07 | Method and system for relieving urban raise dust based on Internet of things monitoring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310070064.8A CN115796423A (en) | 2023-02-07 | 2023-02-07 | Method and system for relieving urban raise dust based on Internet of things monitoring |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115796423A true CN115796423A (en) | 2023-03-14 |
Family
ID=85430147
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310070064.8A Pending CN115796423A (en) | 2023-02-07 | 2023-02-07 | Method and system for relieving urban raise dust based on Internet of things monitoring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115796423A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116862266A (en) * | 2023-07-25 | 2023-10-10 | 南京创蓝科技有限公司 | Method and system for improving operation precision of fog gun vehicle based on big data |
| CN116973522A (en) * | 2023-09-22 | 2023-10-31 | 深圳市天地互通科技有限公司 | Integrated automatic atmosphere monitoring control system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708698A (en) * | 2012-06-12 | 2012-10-03 | 北京理工大学 | Vehicle optimal-path navigation method based on vehicle internet |
| CN111461624A (en) * | 2020-04-20 | 2020-07-28 | 上海东普信息科技有限公司 | Logistics line planning method, device, equipment and storage medium |
| CN112729380A (en) * | 2020-12-11 | 2021-04-30 | 边缘智能研究院南京有限公司 | Urban digital information real-time acquisition system |
| CN114330874A (en) * | 2021-12-28 | 2022-04-12 | 扬州大学 | Vehicle collecting and transporting scheduling method and system based on urban household garbage classification |
| CN114429315A (en) * | 2022-04-06 | 2022-05-03 | 成都秦川物联网科技股份有限公司 | Smart city dust pollution management method and Internet of things system |
| CN115355922A (en) * | 2022-08-16 | 2022-11-18 | 山东劳动职业技术学院（山东劳动技师学院） | Travel path planning method and system based on improved ant colony algorithm |
| CN115471153A (en) * | 2022-08-27 | 2022-12-13 | 贵州通村村科技发展有限公司 | New rural wisdom logistics management system |
-
2023
- 2023-02-07 CN CN202310070064.8A patent/CN115796423A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708698A (en) * | 2012-06-12 | 2012-10-03 | 北京理工大学 | Vehicle optimal-path navigation method based on vehicle internet |
| CN111461624A (en) * | 2020-04-20 | 2020-07-28 | 上海东普信息科技有限公司 | Logistics line planning method, device, equipment and storage medium |
| CN112729380A (en) * | 2020-12-11 | 2021-04-30 | 边缘智能研究院南京有限公司 | Urban digital information real-time acquisition system |
| CN114330874A (en) * | 2021-12-28 | 2022-04-12 | 扬州大学 | Vehicle collecting and transporting scheduling method and system based on urban household garbage classification |
| CN114429315A (en) * | 2022-04-06 | 2022-05-03 | 成都秦川物联网科技股份有限公司 | Smart city dust pollution management method and Internet of things system |
| CN115355922A (en) * | 2022-08-16 | 2022-11-18 | 山东劳动职业技术学院（山东劳动技师学院） | Travel path planning method and system based on improved ant colony algorithm |
| CN115471153A (en) * | 2022-08-27 | 2022-12-13 | 贵州通村村科技发展有限公司 | New rural wisdom logistics management system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116862266A (en) * | 2023-07-25 | 2023-10-10 | 南京创蓝科技有限公司 | Method and system for improving operation precision of fog gun vehicle based on big data |
| CN116973522A (en) * | 2023-09-22 | 2023-10-31 | 深圳市天地互通科技有限公司 | Integrated automatic atmosphere monitoring control system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115796423A (en) | Method and system for relieving urban raise dust based on Internet of things monitoring | |
| CN106205114B (en) | A kind of Freeway Conditions information real time acquiring method based on data fusion | |
| CN102737510B (en) | Real-time traffic condition acquisition method based on mobile intelligent terminal | |
| US20170091350A1 (en) | Near real-time modeling of pollution dispersion | |
| Voukantsis et al. | Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2. 5 concentrations using artificial neural networks, in Thessaloniki and Helsinki | |
| CN106529608B (en) | A kind of telemetering motor vehicle tail equipment complex system for arranging gravity points | |
| CN102708698B (en) | Vehicle optimal-path navigation method based on vehicle internet | |
| CN106097755A (en) | For identifying the method parked place and/or vacate place | |
| CN104121918A (en) | Real-time path planning method and system | |
| CN114429315B (en) | Smart city dust pollution management method and Internet of things system | |
| CN102779410A (en) | Parallel implementation method of multi-source heterogeneous traffic data fusion | |
| CN105241524A (en) | Urban flood warning system and method based on radial basic function neural network model | |
| US11486718B2 (en) | Predicting vehicle travel time on routes of unbounded length in arterial roads | |
| CN114842349B (en) | Building construction environment protection method and system based on information technology | |
| CN110738354B (en) | Method and device for predicting particulate matter concentration, storage medium and electronic equipment | |
| CN115481987B (en) | Smart city street management method, internet of things system, device and storage medium | |
| CN117173871B (en) | Flood prevention monitoring method and system | |
| CN107886188A (en) | Liquefied natural gas public transport exhaust emissions Forecasting Methodology | |
| CN117708617B (en) | Atmospheric pollution tracing method based on multi-source big data and pollution characteristic space-time matching | |
| CN115796034A (en) | Road dust contribution assessment system and method based on machine learning and numerical mode | |
| ShirMohammadi et al. | The traffic congestion analysis using traffic congestion index and artificial neural network in main streets of electronic city (case study: Hamedan city) | |
| CN117546218A (en) | Distributed multitasking machine learning for traffic prediction | |
| CN107066501A (en) | A kind of telemetering motor vehicle tail equipment points distributing method based on road similitude | |
| Raiyn | Speed adaptation in urban road network management | |
| CN114993336B (en) | Commuting path optimization method and system based on PM2.5 pollutant exposure risk |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230314 |
|
| RJ01 | Rejection of invention patent application after publication |