CN115235614A - Urban environmental noise real-time monitoring method, system, equipment and storage medium - Google Patents
Urban environmental noise real-time monitoring method, system, equipment and storage medium Download PDFInfo
- Publication number
- CN115235614A CN115235614A CN202211161652.4A CN202211161652A CN115235614A CN 115235614 A CN115235614 A CN 115235614A CN 202211161652 A CN202211161652 A CN 202211161652A CN 115235614 A CN115235614 A CN 115235614A
- Authority
- CN
- China
- Prior art keywords
- noise
- data
- urban
- traffic
- real
- 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
- 230000007613 environmental effect Effects 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000012544 monitoring process Methods 0.000 title claims abstract description 40
- 238000012545 processing Methods 0.000 claims abstract description 17
- 230000002093 peripheral effect Effects 0.000 claims description 18
- 230000005855 radiation Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 14
- 150000001875 compounds Chemical class 0.000 claims description 3
- 238000005070 sampling Methods 0.000 description 12
- 238000001514 detection method Methods 0.000 description 6
- 238000010276 construction Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000012806 monitoring device Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000004308 accommodation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
The invention relates to the technical field of environmental protection, in particular to a method, a system, equipment and a storage medium for monitoring urban environmental noise in real time, which comprise the following steps: collecting urban traffic real-time data and urban surrounding environment measuring point noise data; according to the urban traffic real-time data and a pre-established traffic noise prediction model, urban traffic noise prediction data are obtained; carrying out structuralization processing on the urban traffic noise prediction data and the noise data of the urban surrounding environment measuring points, and collecting and storing the data in a large noise data platform; and acquiring the geographical position input by the user, inquiring a noisy big data platform by taking the geographical position as index information, and judging the noise pollution degree. According to the invention, the noise big data platform which has the widest coverage range and has universality and real-time performance is realized by fusing urban traffic data and data acquired by fixed and mobile monitoring equipment, the intelligent degree of the system is improved, and scientific basis and data support are provided for environmental protection departments.
Description
Technical Field
The invention relates to the technical field of environmental protection, in particular to a method, a system, equipment and a storage medium for monitoring urban environmental noise in real time.
Background
In order to implement the environmental noise pollution prevention and control law of the people's republic of China, accelerate the construction of quiet and beautiful ecological environment and accelerate the improvement of self capacity construction, strengthen the noise pollution prevention and control work, improve the sound environment quality of cities and villages, and start the construction of a modern automatic environment monitoring system, along with the development of cities, the construction of an automatic environment noise monitoring system is gradually promoted, the noise monitoring automation level and the precision and frequency of the environmental noise measurement are improved, and a noise map is drawn, so that the method has very important practical significance for promoting the technical progress and the scientific and technological development in the environmental protection field and improving the urban environment monitoring and management capacity.
However, the current environmental noise has the following drawbacks:
1) The environmental noise detection can realize 7-24-hour operation by detecting the environmental noise through fixed detection stations and skynet systems set by government environmental protection, urban management departments and scientific research institutes, but the construction, operation and maintenance costs are very high, and the data scale and value are very limited due to the small geographic coverage range.
2) Environmental noise is detected through professional portable equipment and certain mobile phone APPs, and the environmental noise detection can realize the real-time detection of ubiquitous environmental noise at a mobile terminal, but the data is highly fragmented, and is not gathered into a valuable large data platform, and is not communicated with the whole technology for collection, storage, analysis and application and a commercial closed loop.
3) The traditional environmental noise detection does not integrate urban traffic data, and urban traffic (roads, subways and high-speed rails) is used as a relatively stable noise emission source, has a certain space-time mode and rule, can be predicted in a data fitting mode, and forms a data source, but the data source is not considered in the current technology.
Disclosure of Invention
The invention provides a method, a system, equipment and a storage medium for monitoring urban environmental noise in real time, and solves the technical problems that noise data cannot be effectively collected in the traditional noise detection method, the geographic coverage range is small, and the noise monitoring error is large due to neglecting urban traffic data.
In order to solve the technical problems, the invention provides a method, a system, equipment and a storage medium for monitoring urban environmental noise in real time.
In a first aspect, the present invention provides a real-time monitoring method for urban environmental noise, which is applied to a noisy big data platform deployed in a cloud, and the method includes the following steps:
collecting urban traffic real-time data and urban surrounding environment measuring point noise data, wherein the urban traffic real-time data comprises an urban road traffic state, a high-speed train schedule and a subway operation schedule;
obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model;
carrying out structuralized processing on the urban traffic noise prediction data and the urban surrounding environment measuring point noise data, and collecting and storing the data on a large noise data platform;
and acquiring the geographical position input by the user, inquiring a noisy big data platform by taking the geographical position as index information, and judging the noise pollution degree.
In a further embodiment, the step of obtaining urban traffic noise prediction data based on the urban traffic real-time data and a pre-established traffic noise prediction model comprises:
collecting road traffic noise data of cities under different urban road traffic states; the urban road traffic state comprises severe road congestion, slow road driving and smooth road;
constructing a road traffic noise prediction model according to the urban road traffic state and the road traffic noise data;
and receiving the urban road traffic state sent by the third-party internet map service provider, and obtaining road noise prediction data according to the road traffic noise prediction model.
In a further embodiment, the road noise prediction data is calculated as:
in the formula (I), the compound is shown in the specification,
representing road noise prediction data in an mth urban road traffic state;
representing n times of sampled road traffic noise data in the mth urban road traffic state;
represents the total number of samples in the mth urban road traffic state.
In a further embodiment, the step of obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model comprises:
in the running process of a high-speed rail train, high-speed rail noise data of peripheral measuring points of a high-speed rail line are collected, wherein the high-speed rail noise data comprise decibels, radiation radius and duration of high-speed rail noise;
constructing a high-speed rail traffic noise prediction model according to the peripheral measuring points of the high-speed rail line and a plurality of high-speed rail noise data corresponding to the peripheral measuring points;
determining the running position of the high-speed train according to the acquired high-speed train timetable;
predicting to obtain high-speed rail noise prediction data according to the running position of the high-speed rail train and a high-speed rail traffic noise prediction model; the high-speed rail noise prediction data comprise prediction decibel mean value, prediction radiation radius and prediction duration of the high-speed rail noise.
In a further embodiment, the step of obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model comprises:
acquiring subway noise data of a subway at a peripheral measuring point of a road surface track in the running process of the subway, wherein the subway noise data comprises the decibel number, the radiation radius and the duration time of subway noise;
according to the peripheral measuring points of the subway on the road surface track and a plurality of subway noise data corresponding to the peripheral measuring points, a subway traffic noise prediction model is constructed;
determining the running position of the subway according to the acquired subway running schedule;
predicting to obtain subway noise prediction data according to the subway running position and a subway traffic noise prediction model; the subway noise prediction data comprise prediction decibel number average value, prediction radiation radius and prediction duration of subway noise.
In a further embodiment, the step of querying a noisy big data platform by using the geographical location as the index information and determining the noise pollution degree comprises:
by taking the geographic position as index information, inquiring a large noise data platform to obtain noise data of measuring points of the urban surrounding environment and urban traffic noise prediction data;
determining a noise pollution coefficient according to the noise data of the measuring points of the urban surrounding environment and the urban traffic noise prediction data; the noise pollution coefficient comprises a maximum noise decibel number, a minimum noise decibel number and an average noise decibel number;
and judging the noise pollution degree according to the noise pollution coefficient.
In further embodiments, the method further comprises:
collecting urban environment noise audio data, and determining the noise pollution type according to the urban environment noise audio data;
and storing the urban environment noise audio data in a noise big data platform based on the noise pollution category.
In a second aspect, the present invention provides a real-time monitoring system for urban environmental noise, the system comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring urban traffic real-time data and urban surrounding environment measuring point noise data, and the urban traffic real-time data comprises an urban road traffic state, a high-speed train timetable and a subway operation timetable;
the noise prediction module is used for obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model;
the noise storage module is used for carrying out structured processing on the urban traffic noise prediction data and the urban surrounding environment measuring point noise data, and collecting and storing the urban traffic noise prediction data and the urban surrounding environment measuring point noise data in a large noise data platform;
and the noise judgment module is used for acquiring the geographical position input by the user, inquiring the noisy big data platform by taking the geographical position as index information, and judging the noise pollution degree.
In a third aspect, the present invention further provides a computer device, including a processor and a memory, where the processor is connected to the memory, the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the computer device executes the steps for implementing the method.
In a fourth aspect, the present invention also provides a computer-readable storage medium, in which a computer program is stored, which computer program, when executed by a processor, implements the steps of the above method.
The invention provides a method, a system, equipment and a storage medium for monitoring urban environmental noise in real time, wherein the method carries out structured processing on urban traffic noise prediction data and noise data of urban surrounding environment measuring points, and collects and stores the data in a large noise data platform, so that a user queries the large noise data platform by taking a geographical position as index information and judges the noise pollution degree, thereby forming the large noise data platform with the widest coverage range and ubiquitous and real-time environmental noise. Compared with the prior art, the method is based on the universality and the real-time performance of the equipment at the mobile end, and the basic data of the existing fixed monitoring equipment is fused to form a real-time environmental noise data panorama with the widest coverage range; meanwhile, urban traffic data are fused, and a urban traffic noise prediction model is fitted by collecting sampling data of urban traffic in different states to form a new prediction data source, so that a noise big data platform is established, and the whole technology and commercial closed loop are collected, stored, analyzed and applied.
Drawings
FIG. 1 is a schematic flow chart of a real-time monitoring method for urban environmental noise according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a noisy big data platform application provided by an embodiment of the present invention;
FIG. 3 is a block diagram of a real-time monitoring system for urban environmental noise according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, which are given solely for the purpose of illustration and are not to be construed as limitations of the present invention, including reference to and illustration of the accompanying drawings, which are not to be construed as limitations of the scope of the invention, since many variations thereof are possible without departing from the spirit and scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides a real-time monitoring method for urban environmental noise, which is applied to a cloud-deployed noise big data platform, and as shown in fig. 1, the method includes the following steps:
s1, collecting urban traffic real-time data and urban surrounding environment measuring point noise data; the urban traffic real-time data comprises urban road traffic states, high-speed train schedules and subway running schedules.
As shown in fig. 2, in this embodiment, noise data of measuring points in the surrounding environment of the city is collected in real time by the fixed noise monitoring device and the mobile noise monitoring device, and simultaneously, real-time data of the urban traffic is collected by the noise collection software, which specifically includes:
A. fixed noise monitoring equipment:
real-time data acquisition and summarization are carried out through fixed monitoring sites and skynet systems set up by government environmental protection departments, urban management departments and scientific research institutes;
the real-time data acquisition and summarization are carried out through an external decibel detector (a communication module) of the existing product (such as a mute cabin) deployed at the client by the doctor.
B. The mobile noise monitoring device:
and (3) carrying out on-site real-time noise acquisition and summarization by using a 'sound manager' mobile phone APP developed by the doctor.
C. By interfacing with third party internet platforms such as: and (4) beautifying the masses, carrying, weChat and the like, and carrying out on-site real-time noise acquisition and summarization.
It should be noted that, because urban traffic (roads, subways, and high-speed railways) is used as a relatively stable noise emission source and has a certain spatio-temporal pattern and rule, this embodiment can predict urban traffic noise data in a data fitting manner to form a data source, thereby avoiding collecting urban traffic data on site, and because the noise data of the roads, the high-speed railways, the subways, and other traffic environment noise are collected in real time, if they are overlapped in geographic location, the data collected in real time is used as the standard.
And S2, obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model.
In this embodiment, the obtaining urban traffic noise prediction data according to the urban traffic real-time data and a traffic noise prediction model established in advance includes:
collecting road traffic noise data of cities under different urban road traffic states; the urban road traffic state comprises severe road congestion, slow road driving and smooth road traffic, and the road traffic noise data comprises road traffic noise decibels;
constructing a road traffic noise prediction model according to the urban road traffic state and the road traffic noise data;
receiving an urban road traffic state sent by a third-party internet map service provider, and obtaining road noise prediction data according to a road traffic noise prediction model, wherein the calculation formula of the road noise prediction data is as follows:
in the formula (I), the compound is shown in the specification,
representing road noise prediction data in an mth urban road traffic state;
representing road traffic noise data sampled n times in the mth type of urban road traffic state;
represents the total number of samples in the mth urban road traffic state.
Specifically, in the embodiment, an expressway, a main road, a secondary road and a branch road of a city are selected as urban road objects, each road is provided with a sampling point at a preset sampling distance, each sampling point needs to measure the noise level of the urban expressway in four states (severe congestion, slow driving and smooth), each state samples for n times (n is more than or equal to 10), and a road traffic noise prediction model is constructed according to the obtained sampling data, in the embodiment, noise measurement is carried out according to the regulations in the general monitoring of urban acoustic environment (HJ 640-2012) and the acoustic environment quality standard (GB 3096-2008) of the environmental noise monitoring technical specifications, wherein the preset sampling distance is preferably set to be 100 meters.
Then, a third-party internet map service provider obtains road traffic states (serious congestion, slow driving and smooth traffic), and road noise prediction data of each sampling point are obtained according to the road traffic noise prediction model
Each sample point S corresponds to a road section (100 meters), wherein the road noise prediction data
The geometric mean value of the road traffic noise data of n times of sampling corresponding to the current state m (serious congestion, slow driving and smooth driving); since the map data is available in real time, the present embodiment can predict the approximate decibels and corresponding pollution levels of the surrounding roads in real time and implement the predictionAnd loading the data into a noisy big data platform.
In this embodiment, the step of obtaining the urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model includes:
in the running process of a high-speed rail train, acquiring high-speed rail noise data of peripheral measuring points of a high-speed rail line, wherein the high-speed rail noise data comprises decibels, radiation radius and duration of high-speed rail noise;
constructing a high-speed rail traffic noise prediction model according to the peripheral measuring points of the high-speed rail line and a plurality of high-speed rail noise data corresponding to the peripheral measuring points;
determining the running position of the high-speed train according to the acquired high-speed train timetable;
predicting to obtain high-speed rail noise prediction data according to the running position of the high-speed rail train and a high-speed rail traffic noise prediction model; the high-speed rail noise prediction data comprise prediction decibel mean value, prediction radiation radius and prediction duration of the high-speed rail noise.
Specifically, in this embodiment, the real-time decibel number, the radiation radius and the duration of noise when the high-speed train passes through the road are obtained by sampling the periphery of the high-speed train, and then the real-time noise distribution condition around the current real-time position line of the high-speed train is predicted and analyzed according to the captured high-speed train schedule, and when the high-speed train passes through a certain place, decibel number (similarly, the same as the calculation method of road noise prediction data, the geometric mean value of n times of data), radiation radius and duration generated when the train passes through the road can be obtained according to n times (n is greater than or equal to 10) of real-time sampling of data.
Since the high-speed rail driving information is publicly available and can be acquired in real time, the real-time noise data of the high-speed rail line can be generated in a prediction mode by the method and loaded to a background big data platform.
In this embodiment, the step of obtaining the urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model includes:
acquiring subway noise data of a subway at peripheral measuring points of a road surface track in the running process of the subway, wherein the subway noise data comprises the decibel number, the radiation radius and the duration time of subway noise;
constructing a subway traffic noise prediction model according to peripheral measuring points of a subway on a road surface track and a plurality of subway noise data corresponding to the peripheral measuring points;
determining the running position of the subway according to the acquired subway running schedule;
predicting to obtain subway noise prediction data according to the subway running position and a subway traffic noise prediction model; the subway noise prediction data comprises prediction decibel number average value, prediction radiation radius and prediction duration of subway noise.
Specifically, similar to a high-speed rail noise data processing mode, the method obtains the decibel number of noise, the radiation radius and the duration time when the subway rail train runs on the road through sampling of the subway on the periphery of the road surface rail, then predicts the decibel number of real-time noise of the subway on the road surface rail according to a publicly available subway running schedule, and loads the decibel number to a background large data platform.
And S3, carrying out structured processing on the urban traffic noise prediction data and the urban surrounding environment measuring point noise data, and collecting and storing the data in a large noise data platform.
In the embodiment, all the real-time environmental noise data and the urban traffic noise prediction data are converted into a unified structural format, including position information, timestamps, decibels and the like, and the geographic position is used as a main index, the structured data is summarized to the big noise data platform, and the big noise data platform is deployed at the cloud end, so that all historical noise data of past detection of the geographic position are reserved.
And S4, acquiring the geographical position input by the user, inquiring a noise big data platform by taking the geographical position as index information, and judging the noise pollution degree.
In this embodiment, the step of querying the noisy big data platform by using the geographic location as the index information and determining the noise pollution degree includes:
inquiring a large noise data platform by taking the geographic position as index information to obtain noise data of measuring points of urban surrounding environment and urban traffic noise prediction data;
determining a noise pollution coefficient according to the noise data of the measuring points of the urban surrounding environment and the urban traffic noise prediction data; the noise pollution coefficient comprises a maximum noise decibel number, a minimum noise decibel number and an average noise decibel number;
and judging the noise pollution degree according to the noise pollution coefficient.
Specifically, the present embodiment analyzes the noise pollution level according to the historical noise data of a certain geographic location, for example, various common statistical indicators such as the maximum decibel number, the minimum decibel number, and the average decibel number of the geographic location are calculated according to the historical noise data of the geographic location within a certain period (three days, one week, one month, etc.), and then the noise pollution level of the location can be roughly determined.
In an embodiment, the method for monitoring urban environmental noise in real time provided by this embodiment further includes:
collecting urban environment noise audio data, and determining the noise pollution type according to the urban environment noise audio data;
and storing the urban environment noise audio data in a noise big data platform based on the noise pollution category.
Specifically, the embodiment can analyze the type of noise according to the audio file imported by the acquisition end, and the noise is used as pollution source information and supplemented to a noise big data platform; it should be noted that, at present, the data collected in this embodiment is only decibels of the environment, which is a numerical value, and only reflects a general pollution level, and if further analysis of a noise pollution source is needed, the collected audio file needs to be transmitted to a platform for intelligent analysis, and noise categories, such as horn sound, electrical transfer sound, pile driver sound, motor vehicle engine sound, and the like, are determined and stored in a noise big data platform based on the determined noise categories, where an application scenario of the noise big data platform constructed in this embodiment includes:
government sector oriented applications: the real noise environment data reference can be provided for environmental protection monitoring, law enforcement and evidence collection and city planning;
business enterprise oriented applications: noise environment evaluation can be provided for office environment, catering consumption, hotel accommodations and the like;
application to the general public: the noise environment assessment can be provided for the demands of common residents on residential districts, house buying, house renting and the like.
The embodiment of the invention provides a real-time monitoring method for urban environmental noise, which fuses urban traffic data, fits an urban traffic noise prediction model by collecting data of urban traffic (roads, subways and high-speed railways) and real-time ground road data sampling, forms a new data source, and forms a wide-coverage environmental noise big data platform with universality and real-time performance by connecting a mobile internet and internet of things fixed equipment. Compared with the prior art, the full-range dynamic monitoring of the urban environmental noise space-time distribution state is realized, meanwhile, the data support is provided for the environmental noise monitoring through the large-noise data platform, and the method has important application value in the fields of urban noise pollution treatment and the like.
It should be noted that, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation to the implementation process of the embodiment of the present application.
In one embodiment, as shown in fig. 3, an embodiment of the present invention provides a real-time monitoring system for urban environmental noise, where the system includes:
the system comprises a data acquisition module 101, a data processing module and a data processing module, wherein the data acquisition module 101 is used for acquiring urban traffic real-time data and urban surrounding environment measuring point noise data, and the urban traffic real-time data comprises an urban road traffic state, a high-speed train schedule and a subway operation schedule;
the noise prediction module 102 is used for obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model;
the noise storage module 103 is used for carrying out structured processing on the urban traffic noise prediction data and the urban surrounding environment measuring point noise data, and collecting and storing the urban traffic noise prediction data and the urban surrounding environment measuring point noise data in a large noise data platform;
and the noise judgment module 104 is configured to acquire a geographical position input by a user, query a noisy data platform by using the geographical position as index information, and judge a noise pollution degree.
For a specific limitation of the real-time monitoring system for urban environmental noise, reference may be made to the above-mentioned limitation on the real-time monitoring method for urban environmental noise, and details thereof are not repeated here. Those of ordinary skill in the art will appreciate that the various modules and steps described in connection with the embodiments disclosed herein may be implemented as hardware, software, or combinations of both. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the invention provides a real-time monitoring system for urban environmental noise, which acquires urban traffic noise prediction data and urban surrounding environment measuring point noise data through a data acquisition module and a noise prediction module; the noise storage module is used for carrying out structuralized processing on urban traffic noise prediction data and urban surrounding environment measuring point noise data, and collecting and storing the data in a noise big data platform; judging the noise pollution degree through a noise judgment module; compared with the prior art, the urban traffic data are fused, the basic data of the existing fixed monitoring equipment are fused based on the universality and the real-time performance of the equipment at the mobile end, and the real-time environmental noise big data platform with the widest coverage range is formed.
FIG. 4 is a computer device including a memory, a processor, and a transceiver connected via a bus according to an embodiment of the present invention; the memory is used to store a set of computer program instructions and data and may transmit the stored data to the processor, which may execute the program instructions stored by the memory to perform the steps of the above-described method.
Wherein the memory may comprise volatile memory or non-volatile memory, or may comprise both volatile and non-volatile memory; the processor may be a central processing unit, a microprocessor, an application specific integrated circuit, a programmable logic device, or a combination thereof. By way of example, and not limitation, the programmable logic devices described above may be complex programmable logic devices, field programmable gate arrays, general array logic, or any combination thereof.
In addition, the memory may be a physically separate unit or may be integrated with the processor.
It will be appreciated by those of ordinary skill in the art that the architecture shown in fig. 4 is a block diagram of only a portion of the architecture associated with the present solution and is not intended to limit the computing devices to which the present solution may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have the same arrangement of components.
In one embodiment, the present invention provides a computer readable storage medium, on which a computer program is stored, and the computer program implements the steps of the above method when executed by a processor.
According to the urban environmental noise real-time monitoring method, the system, the equipment and the storage medium, the mobile internet and the internet of things fixed equipment are connected to monitor urban environmental noise data, and urban traffic data are fused, so that an environmental noise data panorama which is widest in coverage range and has universality and instantaneity is formed, and technical support is provided for urban environmental governance decision making.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), among others.
Those skilled in the art will appreciate that all or part of the processes in the methods according to the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and the computer program can include the processes according to the embodiments of the methods described above when executed.
The above-mentioned embodiments only express some preferred embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these should be construed as the protection scope of the present application. Therefore, the protection scope of the present patent application shall be subject to the protection scope of the claims.
Claims (10)
1. A real-time monitoring method for urban environmental noise is applied to a noise big data platform deployed at a cloud end, and comprises the following steps:
collecting urban traffic real-time data and urban surrounding environment measuring point noise data, wherein the urban traffic real-time data comprises an urban road traffic state, a high-speed train schedule and a subway operation schedule;
obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model;
carrying out structuralization processing on the urban traffic noise prediction data and the noise data of the urban surrounding environment measuring points, and collecting and storing the data in a large noise data platform;
and acquiring the geographical position input by the user, inquiring a noisy big data platform by taking the geographical position as index information, and judging the noise pollution degree.
2. The method according to claim 1, wherein said step of obtaining urban traffic noise prediction data based on said urban traffic real-time data and a pre-established traffic noise prediction model comprises:
collecting road traffic noise data of cities under different urban road traffic states; the urban road traffic state comprises severe road congestion, slow road driving and smooth road;
constructing a road traffic noise prediction model according to the urban road traffic state and the road traffic noise data;
and receiving the urban road traffic state sent by the third-party internet map service provider, and obtaining road noise prediction data according to the road traffic noise prediction model.
3. The real-time urban environmental noise monitoring method according to claim 2, wherein the road noise prediction data is calculated by the formula:
in the formula (I), the compound is shown in the specification,
representing road noise prediction data in an mth urban road traffic state;
representing road traffic noise data sampled n times in the mth type of urban road traffic state;
represents the total number of samples in the mth urban road traffic state.
4. The method for real-time monitoring urban environmental noise according to claim 1, wherein said step of obtaining urban traffic noise prediction data from said urban traffic real-time data and a pre-established traffic noise prediction model comprises:
in the running process of a high-speed rail train, high-speed rail noise data of peripheral measuring points of a high-speed rail line are collected, wherein the high-speed rail noise data comprise decibels, radiation radius and duration of high-speed rail noise;
constructing a high-speed rail traffic noise prediction model according to the peripheral measuring points of the high-speed rail line and a plurality of high-speed rail noise data corresponding to the peripheral measuring points;
determining the running position of the high-speed train according to the acquired high-speed train timetable;
predicting to obtain high-speed rail noise prediction data according to the running position of the high-speed rail train and a high-speed rail traffic noise prediction model; the high-speed rail noise prediction data comprise prediction decibel mean value, prediction radiation radius and prediction duration of the high-speed rail noise.
5. The method according to claim 1, wherein said step of obtaining urban traffic noise prediction data based on said urban traffic real-time data and a pre-established traffic noise prediction model comprises:
acquiring subway noise data of a subway at peripheral measuring points of a road surface track in the running process of the subway, wherein the subway noise data comprises the decibel number, the radiation radius and the duration time of subway noise;
according to the peripheral measuring points of the subway on the road surface track and a plurality of subway noise data corresponding to the peripheral measuring points, a subway traffic noise prediction model is constructed;
determining the running position of the subway according to the acquired subway running schedule;
predicting to obtain subway noise prediction data according to the subway running position and a subway traffic noise prediction model; the subway noise prediction data comprises prediction decibel number average value, prediction radiation radius and prediction duration of subway noise.
6. The method for monitoring the noise of the urban environment in real time according to claim 1, wherein the step of querying a noisy big data platform by using the geographical location as the index information and judging the noise pollution degree comprises:
inquiring a large noise data platform by taking the geographic position as index information to obtain noise data of measuring points of urban surrounding environment and urban traffic noise prediction data;
determining a noise pollution coefficient according to the noise data of the measuring points of the urban surrounding environment and the urban traffic noise prediction data; the noise pollution coefficient comprises a maximum noise decibel number, a minimum noise decibel number and an average noise decibel number;
and judging the noise pollution degree according to the noise pollution coefficient.
7. The method for monitoring urban environmental noise in real time according to claim 1, further comprising:
collecting urban environment noise audio data, and determining the noise pollution type according to the urban environment noise audio data;
and storing the urban environment noise audio data in a noise big data platform based on the noise pollution category.
8. A real-time monitoring system for urban environmental noise, characterized in that the system comprises:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring urban traffic real-time data and urban surrounding environment measuring point noise data, and the urban traffic real-time data comprises an urban road traffic state, a high-speed train schedule and a subway operation schedule;
the noise prediction module is used for obtaining urban traffic noise prediction data according to the urban traffic real-time data and a pre-established traffic noise prediction model;
the noise storage module is used for carrying out structured processing on the urban traffic noise prediction data and the urban surrounding environment measuring point noise data, and collecting and storing the urban traffic noise prediction data and the urban surrounding environment measuring point noise data in a large noise data platform;
and the noise judgment module is used for acquiring the geographical position input by the user, inquiring the noisy data platform by taking the geographical position as index information, and judging the noise pollution degree.
9. A computer device, characterized by: comprising a processor and a memory, the processor being connected to the memory, the memory being adapted to store a computer program, the processor being adapted to execute the computer program stored in the memory to cause the computer device to perform the method of any of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored thereon a computer program which, when executed, implements the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211161652.4A CN115235614A (en) | 2022-09-23 | 2022-09-23 | Urban environmental noise real-time monitoring method, system, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211161652.4A CN115235614A (en) | 2022-09-23 | 2022-09-23 | Urban environmental noise real-time monitoring method, system, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115235614A true CN115235614A (en) | 2022-10-25 |
Family
ID=83666991
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211161652.4A Pending CN115235614A (en) | 2022-09-23 | 2022-09-23 | Urban environmental noise real-time monitoring method, system, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115235614A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116086596A (en) * | 2022-12-09 | 2023-05-09 | 广州声博士声学技术有限公司 | Intelligent noise detection method and device, computer equipment and storage medium |
| CN117576914A (en) * | 2024-01-15 | 2024-02-20 | 交通运输部科学研究院 | Traffic noise map drawing method and system driven by online traffic index |
| CN117690303B (en) * | 2024-02-04 | 2024-04-26 | 四川三元环境治理股份有限公司 | Noise early warning system, device and early warning method based on traffic data acquisition |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08105792A (en) * | 1994-10-05 | 1996-04-23 | Nitsutoubou Onkyo Eng Kk | Method for measuring noise and/or oscillation of railroad running train |
| KR20050110536A (en) * | 2004-05-19 | 2005-11-23 | 한국철도기술연구원 | The train environment noise measurement method which uses a sound arresting predition algorithm and it comes true the archival medium which is the program for of reading with the computer which records |
| CN103440411A (en) * | 2013-08-15 | 2013-12-11 | 中山大学 | Traffic noise pollution model based on exposed crowd/area/acoustic environment functional area |
| US20150110276A1 (en) * | 2012-04-03 | 2015-04-23 | Budapesti Muszaki Es Gazdasagtudomanyi Egyetem | Method and system for source selective real-time monitoring and mapping of environmental noise |
| CN104598757A (en) * | 2015-02-12 | 2015-05-06 | 西南交通大学 | Method for predicting traffic noise of rail regions |
| CN105930923A (en) * | 2016-04-15 | 2016-09-07 | 东南大学 | Urban road greenbelt noise reduction guiding optimization control method based on 3D noise map |
| CN107705566A (en) * | 2017-10-20 | 2018-02-16 | 吉利汽车研究院（宁波）有限公司 | A kind of City Road Traffic Noise Prediction method and system |
| CN109405958A (en) * | 2018-09-29 | 2019-03-01 | 北京交通大学 | Spatial noise real-time monitoring system in a kind of track traffic station station |
| CN111428285A (en) * | 2020-03-12 | 2020-07-17 | 深圳小库科技有限公司 | Noise evaluation method and device and storage medium |
| CN111753432A (en) * | 2020-07-01 | 2020-10-09 | 合肥学院 | Noise reduction method for urban tramcar sound |
| CN111982273A (en) * | 2020-07-06 | 2020-11-24 | 北京交通大学 | Noise monitoring method for rail transit station platform |
| CN112035590A (en) * | 2020-09-02 | 2020-12-04 | 广州声博士声学技术有限公司 | Electronic noise map display method, electronic noise map display device, computer equipment and storage medium |
| AU2020103374A4 (en) * | 2020-11-11 | 2021-01-28 | Saurabh Gupta | Mobile traffic noise measurement and prediction method using machine learning algorithms |
| CN114485916A (en) * | 2022-01-12 | 2022-05-13 | 广州声博士声学技术有限公司 | Environmental noise monitoring method and system, computer equipment and storage medium |
| CN115083394A (en) * | 2022-08-22 | 2022-09-20 | 广州声博士声学技术有限公司 | Real-time environmental noise identification method, system and equipment integrating space-time attributes |
-
2022
- 2022-09-23 CN CN202211161652.4A patent/CN115235614A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08105792A (en) * | 1994-10-05 | 1996-04-23 | Nitsutoubou Onkyo Eng Kk | Method for measuring noise and/or oscillation of railroad running train |
| KR20050110536A (en) * | 2004-05-19 | 2005-11-23 | 한국철도기술연구원 | The train environment noise measurement method which uses a sound arresting predition algorithm and it comes true the archival medium which is the program for of reading with the computer which records |
| US20150110276A1 (en) * | 2012-04-03 | 2015-04-23 | Budapesti Muszaki Es Gazdasagtudomanyi Egyetem | Method and system for source selective real-time monitoring and mapping of environmental noise |
| CN103440411A (en) * | 2013-08-15 | 2013-12-11 | 中山大学 | Traffic noise pollution model based on exposed crowd/area/acoustic environment functional area |
| CN104598757A (en) * | 2015-02-12 | 2015-05-06 | 西南交通大学 | Method for predicting traffic noise of rail regions |
| CN105930923A (en) * | 2016-04-15 | 2016-09-07 | 东南大学 | Urban road greenbelt noise reduction guiding optimization control method based on 3D noise map |
| CN107705566A (en) * | 2017-10-20 | 2018-02-16 | 吉利汽车研究院（宁波）有限公司 | A kind of City Road Traffic Noise Prediction method and system |
| CN109405958A (en) * | 2018-09-29 | 2019-03-01 | 北京交通大学 | Spatial noise real-time monitoring system in a kind of track traffic station station |
| CN111428285A (en) * | 2020-03-12 | 2020-07-17 | 深圳小库科技有限公司 | Noise evaluation method and device and storage medium |
| CN111753432A (en) * | 2020-07-01 | 2020-10-09 | 合肥学院 | Noise reduction method for urban tramcar sound |
| CN111982273A (en) * | 2020-07-06 | 2020-11-24 | 北京交通大学 | Noise monitoring method for rail transit station platform |
| CN112035590A (en) * | 2020-09-02 | 2020-12-04 | 广州声博士声学技术有限公司 | Electronic noise map display method, electronic noise map display device, computer equipment and storage medium |
| AU2020103374A4 (en) * | 2020-11-11 | 2021-01-28 | Saurabh Gupta | Mobile traffic noise measurement and prediction method using machine learning algorithms |
| CN114485916A (en) * | 2022-01-12 | 2022-05-13 | 广州声博士声学技术有限公司 | Environmental noise monitoring method and system, computer equipment and storage medium |
| CN115083394A (en) * | 2022-08-22 | 2022-09-20 | 广州声博士声学技术有限公司 | Real-time environmental noise identification method, system and equipment integrating space-time attributes |
Non-Patent Citations (1)
| Title |
|---|
| 郝苏申 等: "城市交通噪声可视化研究", 《南京工程学院学报(自然科学版)》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116086596A (en) * | 2022-12-09 | 2023-05-09 | 广州声博士声学技术有限公司 | Intelligent noise detection method and device, computer equipment and storage medium |
| CN116086596B (en) * | 2022-12-09 | 2023-10-27 | 广州声博士声学技术有限公司 | Intelligent noise detection method and device, computer equipment and storage medium |
| CN117576914A (en) * | 2024-01-15 | 2024-02-20 | 交通运输部科学研究院 | Traffic noise map drawing method and system driven by online traffic index |
| CN117576914B (en) * | 2024-01-15 | 2024-03-22 | 交通运输部科学研究院 | Traffic noise map drawing method and system driven by online traffic index |
| CN117690303B (en) * | 2024-02-04 | 2024-04-26 | 四川三元环境治理股份有限公司 | Noise early warning system, device and early warning method based on traffic data acquisition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115235614A (en) | Urban environmental noise real-time monitoring method, system, equipment and storage medium | |
| RU2489681C2 (en) | Method and machine for generation of mapping data and method and navigation device for route detection, using mapping data | |
| CN107845259B (en) | Bus running condition real-time feedback system and bus real-time running data processing method | |
| US9797740B2 (en) | Method of determining trajectories through one or more junctions of a transportation network | |
| US20160123744A1 (en) | Method of creating map data comprising transit times for intersections | |
| Wang et al. | Estimating dynamic origin-destination data and travel demand using cell phone network data | |
| McArdle et al. | Improving the veracity of open and real-time urban data | |
| CN108492558B (en) | Expressway travel reservation method, storage medium and terminal | |
| CN111294730B (en) | Method and device for processing network problem complaint information | |
| JP3818931B2 (en) | Weather forecast system | |
| Duisebekova et al. | Environmental monitoring system for analysis of climatic and ecological changes using LoRa technology | |
| US10121212B1 (en) | System and method for transportation demand management | |
| Vitale et al. | A smartphone based DSS platform for assessing transit service attributes | |
| Klandev et al. | Parking Availability Prediction Using Traffic Data Services | |
| Doulabi | Contextual Adjustment of the ITE Trip Generation Rates Using Wi-Fi and Bluetooth Technologies | |
| CN109855632A (en) | Vehicle pass-through navigation system and method based on the acquisition of bridge damper damping data | |
| Herrnleben et al. | Investigating the Predictability of QoS Metrics in Cellular Networks | |
| Islam-Khan et al. | Predicting Travel Times in Dense and Highly Varying Road Traffic Networks using STARIMA Models. | |
| ARIEF | Origin-Destination (OD) Estimation of the Small Bus and Paratransit Passenger Using Wi-Fi Scanner | |
| CN115665669A (en) | Vehicle track fitting method and device in expressway network based on mobile positioning | |
| CN117793627A (en) | Network signal map construction, network signal strength determination method and electronic equipment | |
| JP2022083592A (en) | Earthquake early-warning system | |
| CN116976493A (en) | Urban rail transit station passenger flow prediction method based on passenger flow progressive decrease rule | |
| Gómez-Torres et al. | Detection Technologies for Dynamic Origin-Destination Matrices and Heavy Vehicles’ Road Selection Studies | |
| CN109951814A (en) | Localization method, device and computer readable storage medium |
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: 20221025 |
|
| RJ01 | Rejection of invention patent application after publication |