CN111124667B - Community noise processing method and system based on edge calculation - Google Patents
Community noise processing method and system based on edge calculation Download PDFInfo
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Abstract
The embodiment of the application provides a community noise processing method and system based on edge computing. The method comprises the following steps: noise edge computing nodes are arranged at noise transmission outlets of each resident in the community, the noise edge computing nodes are arranged facing the direction of a resident sound source, and the size and the direction of noise are collected through rotation; when the collected noise exceeds a noise notification threshold, the size and the azimuth of a noise source are diffused to neighboring noise edge computing nodes through an edge computing network; when the acquired noise exceeds a first noise control threshold, triggering a noise edge computing node corresponding to a noise source to execute a noise source control strategy, and sending a notification signal to a neighbor noise edge computing node; and after receiving the notification signal, the neighbor noise edge computing node triggers the neighbor noise edge computing node to execute a noise propagation control strategy when the noise exceeds a second noise control threshold. The noise processing efficiency is improved through the edge computing technology.
Description
Technical Field
The application relates to the field of edge computing and learning monitoring, in particular to a community noise processing method and system based on edge computing.
Background
At present, in communities, residents often send out noise in unsuitable time periods, and normal rest of neighbors is affected. In addition, sometimes the noise source is hidden, and the neighborhood does not know where the noise source is, and cannot handle the noise.
Moreover, conventionally, from noise generation to neighborhood reaction to property, the property sends out personnel to process, which requires a relatively long period of time, perhaps until the property personnel go to the noise source to process the noise, the noise is no longer generated, and normal work and rest of people are disturbed by short noise, which is an urgent need for a method that can immediately perform simple noise processing even if the noise source is identified.
Edge computing refers to providing recent services nearby on the side near the object or data source, using an open platform with integrated network, computing, storage, and application core capabilities. Edge computing forms a three-layer system structure of an intelligent terminal-edge server-cloud data center by giving certain computing power and storage power to network edge equipment, and provides communication and IT service, storage and computing resources at the edge of the network so as to reduce processing delay of application and more effectively utilize the mobile network.
Disclosure of Invention
Accordingly, an object of the present application is to provide a method and a system for community noise processing based on edge computation, which improve noise processing efficiency and solve the technical problem of low noise processing efficiency in the current community management process.
Based on the above objects, the present application proposes a community noise processing method based on edge computing, including:
noise edge computing nodes are arranged at noise transmission outlets of each resident in the community, the noise edge computing nodes are arranged facing the direction of a resident sound source, and the size and the direction of noise are collected through rotation;
when the collected noise exceeds a noise notification threshold, the size and the azimuth of a noise source are diffused to neighboring noise edge computing nodes through an edge computing network and are directly sent to a community manager;
when the acquired noise exceeds a first noise control threshold, triggering a noise edge computing node corresponding to the noise source to execute a noise source control strategy, and sending a notification signal to a neighbor noise edge computing node;
and after receiving the notification signal, the neighbor noise edge computing node continuously monitors the noise size and the direction, and when the noise size exceeds a second noise control threshold value, the neighbor noise edge computing node is triggered to execute a noise transmission control strategy.
In some embodiments, the method further comprises:
setting a noise frequency threshold, and when the frequency of the discrete noise source exceeds the noise frequency threshold, diffusing the size, frequency and direction of the noise source to the adjacent noise edge computing nodes through the edge computing network and directly sending the noise source to a community manager.
In some embodiments, the method further comprises:
each noise edge computing node records the noise generation record of the corresponding resident, and regularly extracts the record to obtain a noise generation rule, and sends the noise generation rule to the neighboring noise edge computing nodes;
and the neighbor noise edge computing node predicts the noise avoidance time according to the noise generation rule and sends a noise avoidance strategy to the corresponding resident.
In some embodiments, a noise edge computing node is disposed at a noise propagation outlet of each household in a community, the noise edge computing node being disposed facing a household sound source direction and collecting a size and an azimuth of noise by rotation, comprising:
a plurality of noise edge computing nodes are installed at the noise transmission outlets of each resident;
the noise edge computing nodes detect the noise emitted by the appointed householder at the same time, calibrate the noise according to all detection results and determine the size and the azimuth of the noise.
In some embodiments, the executing the noise source control strategy comprises:
sending reminding information to households corresponding to the noise sources;
releasing the sound deadening substance toward the noise source.
In some embodiments, sending a notification signal to a neighbor noise edge computing node includes:
transmitting notification signals according to different priorities according to the noise size, direction and distance of the noise source, wherein the priorities are determined according to the following formula:
wherein P is the priority, DB is the noise size of the noise source, an is the included angle between the neighboring noise edge computing node and the noise source connecting line as well as the noise source noise propagation center line, and Dis is the distance between the neighboring noise edge computing node and the noise source.
Based on the above object, the present application further provides a community noise processing system based on edge computing, including:
the construction module is used for setting noise edge computing nodes at noise transmission outlets of each resident in the community, wherein the noise edge computing nodes are arranged facing the direction of the resident sound source and collect the size and the direction of noise through rotation;
the acquisition module is used for diffusing the size and the direction of the noise source to the neighboring noise edge computing nodes through the edge computing network when the acquired noise exceeds the noise notification threshold value, and directly transmitting the noise source to a community manager;
the first control module is used for triggering the noise edge computing node corresponding to the noise source to execute a noise source control strategy when the acquired noise exceeds a first noise control threshold value, and sending a notification signal to the neighbor noise edge computing node;
and the second control module is used for continuously monitoring the noise size and the direction after the neighbor noise edge computing node receives the notification signal, and triggering the neighbor noise edge computing node to execute a noise propagation control strategy when the noise size exceeds a second noise control threshold.
In some embodiments, the system further comprises:
the frequency detection module is used for setting a noise frequency threshold, and when the frequency of the discrete noise source exceeds the noise frequency threshold, the magnitude, the frequency and the direction of the noise source are diffused to the adjacent noise edge computing nodes through the edge computing network and are directly sent to a community manager.
In some embodiments, the system further comprises:
the rule extraction module is used for recording the noise generation record of the corresponding resident by each noise edge calculation node, carrying out rule extraction on the record to obtain a noise generation rule, and sending the noise generation rule to the adjacent noise edge calculation nodes;
and the avoidance strategy module is used for predicting the avoidance noise time according to the noise generation rule by the neighbor noise edge computing node and sending a noise avoidance strategy to the corresponding resident.
In some embodiments, the building block comprises:
an initial unit for installing a plurality of noise edge calculation nodes at noise propagation outlets of each resident;
and the calibration unit is used for simultaneously detecting the noise sent by the appointed householder by the plurality of noise edge calculation nodes, calibrating according to all detection results and determining the size and the azimuth of the noise.
Drawings
In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.
FIG. 1 illustrates a flow chart of a community noise processing method based on edge computing, according to an embodiment of the invention.
FIG. 2 illustrates a flow chart of a community noise processing method based on edge computing, according to an embodiment of the invention.
FIG. 3 illustrates a flow chart of a community noise processing method based on edge computing, according to an embodiment of the invention.
FIG. 4 illustrates a block diagram of a community noise processing system based on edge computing in accordance with an embodiment of the present invention.
FIG. 5 illustrates a block diagram of a community noise processing system based on edge computing in accordance with an embodiment of the present invention.
FIG. 6 illustrates a block diagram of a community noise processing system based on edge computing in accordance with an embodiment of the present invention.
Fig. 7 shows a constitution diagram of a building block according to an embodiment of the present invention.
Fig. 8 shows a schematic diagram according to an embodiment of the invention.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 illustrates a flow chart of a community noise processing method based on edge computing, according to an embodiment of the invention. As shown in fig. 1, the community noise processing method based on edge calculation includes:
step S11, noise edge computing nodes are arranged at noise transmission outlets of all households in the community, the noise edge computing nodes are arranged facing the sound source direction of the households, and the size and the direction of noise are collected through rotation.
For example, the noise edge computing node may be located at the opposite position of the doorway of each resident in the community, or may be located outside the window or balcony of the resident's home, because these positions are all the positions where noise can be directly transmitted to the outside, and are also the optimal positions for noise monitoring and noise processing.
Moreover, the calculated angles of the noise edges can be automatically adjusted according to the size and direction of the noise. For example, the noise edge computing node may be mounted on a rotatable radar type stand, and when noise is detected, 360 degrees of rotation is performed, an angle is selected that can most accurately monitor the direction and magnitude of the noise source, then a period of time is continuously collected, and a final noise detection finger is obtained after the monitored data enter a stable state, so that the noise source is monitored more accurately.
In one embodiment, a noise edge computing node is disposed at a noise propagation outlet of each resident in a community, the noise edge computing node being disposed facing a direction of a sound source of the resident and collecting a size and a direction of noise by rotation, comprising:
a plurality of noise edge computing nodes are installed at the noise transmission outlets of each resident;
the noise edge computing nodes detect the noise emitted by the appointed householder at the same time, calibrate the noise according to all detection results and determine the size and the azimuth of the noise.
Specifically, a plurality of noise edge calculation nodes may be provided at the noise propagation outlet of the resident. For example, one or more noise edge computing nodes may be located directly opposite the doorway of the resident, and one or more noise edge computing nodes may be located outside the resident's balcony and window.
As shown in fig. 8, S is a noise source, the fan-shaped ASC is a diffusion direction of the noise source S, SB is a center line of noise diffusion, so one or more noise edge computing nodes may be disposed on the fan-shaped edge ABC, preferably, one noise edge computing node is disposed at the position C, so that the noise size, the direction and the kind of S can be monitored most accurately.
Integrating the monitoring data of multiple noise edge computation nodes (e.g., weighted averaging the monitoring data of all noise edge computation nodes) can more accurately determine the magnitude and direction of the noise source.
On the other hand, the monitoring data of one noise edge computing node can be used as reference data, and the monitoring data of other noise edge computing nodes can be used for correction, so that the size and the direction of the noise source can be determined more accurately. For example, the monitoring data of the noise edge computing node at the position opposite to the gate of the resident is used as a reference, and the monitoring data of the noise edge computing node at the position opposite to the gate is corrected by using the monitoring data of the noise edge computing node outside the resident window and the balcony.
And step S12, when the acquired noise exceeds a noise notification threshold, the size and the azimuth of the noise source are diffused to the neighboring noise edge computing nodes through the edge computing network, and the noise source is directly sent to a community manager.
Specifically, the noise can be divided into a plurality of layers according to the size of the noise, for example, the noise is generated just but tends to be larger, and at this time, when the size of the noise exceeds the notification threshold, the noise can be notified to the neighbors or community manager in advance, thereby preventing the noise size from further deteriorating and affecting the normal life of the community residents.
And S13, triggering a noise edge computing node corresponding to the noise source to execute a noise source control strategy when the acquired noise exceeds a first noise control threshold, and sending a notification signal to a neighbor noise edge computing node.
For example, when the monitored noise reaches a certain level, intervention may be immediately performed by some operation of the noise edge computation node, thereby preventing the noise from affecting surrounding neighbors. For example, when it is detected that there is a noise source that is boring a hole, it is possible that the noise source just strikes two holes, and when the community manager arrives, the hole has been struck, the neighbor has been awakened, and the community manager is also on the fly. In order to solve the problem, when the noise edge computing node monitors that the noise source is punching, the control strategy is started immediately, so that the noise can be processed in the shortest time, the noise processing efficiency is improved, and the noise processing effect is improved.
In one embodiment, the executing the noise source control strategy includes:
sending reminding information to households corresponding to the noise sources;
releasing the sound deadening substance toward the noise source.
In particular, from the experience of noise handling, the control noise can be controlled from three angles, namely the source of the noise, the path of the noise and the receiver of the noise, and thus the noise source control strategy also includes these three aspects. On the one hand, noise control is carried out from the angle of a noise receiver, for example, noise avoidance suggestions can be sent to neighbor households according to the size, the type and the direction of the noise source in an intelligent audio-visual mode; on the other hand, the noise control is performed from the noise transmission source, for example, the noise generation reminding can be sent to the noise transmission source by means of intelligent audio-visual mode according to the size and the type of the noise source and combining the living habit, living requirement and community standardization of the surrounding neighbors.
In addition, the noise reducing substance may be released toward the noise source. For example, noise-reducing substances may be sprayed toward the noise source, and for example, a sound-insulating roll-up door at a designated location in the pull-down community may be controlled to block the transmission of noise.
In one embodiment, sending a notification signal to a neighbor noise edge computing node comprises:
transmitting notification signals according to different priorities according to the noise size, direction and distance of the noise source, wherein the priorities are determined according to the following formula:
wherein P is the priority, DB is the noise size of the noise source, an is the included angle between the neighboring noise edge computing node and the noise source connecting line as well as the noise source noise propagation center line, and Dis is the distance between the neighboring noise edge computing node and the noise source.
In addition, in the noise edge computing network composed of noise edge computing nodes, after the noise state is collected and analyzed, the noise state is transmitted to a plurality of neighbor edge computing nodes appointed in the edge learning network in a directional multicast mode, and a traditional broadcasting mode is not adopted, because on one hand, a local edge computing server has a certain computing capacity, and can analyze and calculate which neighbor edge servers need the data; on the other hand, the network load can be greatly reduced by adopting the multicast mode for transmission, and the network congestion is avoided.
And S14, continuously monitoring the noise size and the direction after the neighbor noise edge computing node receives the notification signal, and triggering the neighbor noise edge computing node to execute a noise propagation control strategy when the noise size exceeds a second noise control threshold.
Specifically, as the noise expands further, the neighboring edge computing nodes may be notified to initiate a noise propagation control strategy, with control of noise propagation at the neighboring edge computing nodes. For example, the silencing equipment can be gradually started in the sequence from near to far with the noise source, so that on one hand, the noise blocking resource is saved, and on the other hand, the rest rights of neighbors in the community are effectively protected.
FIG. 2 illustrates a flow chart of a community noise processing method based on edge computing, according to an embodiment of the invention. As shown in fig. 2, the community noise processing method based on edge computing further includes:
and S15, setting a noise frequency threshold, and when the frequency of the discrete noise source exceeds the noise frequency threshold, diffusing the size, frequency and direction of the noise source to the adjacent noise edge computing nodes through the edge computing network and directly sending the noise source to a community manager.
For example, there is a form of noise, which, although the magnitude of noise is insufficient to affect normal work and life of people, sounds with a certain frequency all the time, which affects normal life of people in the community. Therefore, besides monitoring the size, the type, the direction and the like of the noise source, the frequency of the noise source can be monitored, so that the normal life of the community households can be more comprehensively protected.
FIG. 3 illustrates a flow chart of a community noise processing method based on edge computing, according to an embodiment of the invention. As shown in fig. 3, the community noise processing method based on edge calculation further includes:
and S16, each noise edge computing node records the noise generation record of the corresponding resident, and performs rule extraction on the record to obtain a noise generation rule, and sends the noise generation rule to the adjacent noise edge computing nodes.
And S17, predicting noise avoidance time by the neighbor noise edge computing node according to the noise generation rule, and sending a noise avoidance strategy to a corresponding resident.
Specifically, noise sources that generate noise in communities generally have regularity, for example, sound is always generated during decoration, so decoration noise of a designated noise source is recorded, and rules are counted, and avoidance advice (for example, advice that a user leaves a residence or turns on a soundproof device for a certain period of time) is issued to neighbors according to the rules, so that the user is minimally disturbed.
FIG. 4 illustrates a block diagram of a community noise processing system based on edge computing in accordance with an embodiment of the present invention. As shown in FIG. 4, the community noise processing system as a whole based on edge computation can be divided into:
a construction module 41, configured to set a noise edge computing node at a noise propagation outlet of each resident in the community, where the noise edge computing node is set to face a direction of a sound source of the resident, and collect a size and a direction of noise by rotation;
the collecting module 42 is configured to diffuse, when the collected noise exceeds the noise notification threshold, the size and the azimuth of the noise source to the neighboring noise edge computing node through the edge computing network, and send the noise source directly to a community manager;
the first control module 43 is configured to trigger a noise edge computing node corresponding to the noise source to execute a noise source control policy when the collected noise exceeds a first noise control threshold, and send a notification signal to a neighboring noise edge computing node;
the second control module 44 is configured to continuously monitor the noise size and the direction of the neighboring noise edge computing node after receiving the notification signal, and trigger the neighboring noise edge computing node to execute a noise propagation control policy when the noise size exceeds a second noise control threshold.
FIG. 5 illustrates a block diagram of a community noise processing system based on edge computing in accordance with an embodiment of the present invention. As shown in FIG. 5, the community noise processing system based on edge computation can be divided into:
the frequency detection module 45 is configured to set a noise frequency threshold, and when the frequency of the discrete noise source exceeds the noise frequency threshold, diffuse the size, frequency and direction of the noise source to the neighboring noise edge computing nodes through the edge computing network, and send the noise source directly to the community manager.
FIG. 6 illustrates a block diagram of a community noise processing system based on edge computing in accordance with an embodiment of the present invention. As shown in FIG. 6, the community noise processing system based on edge computation can be divided into:
the rule extraction module 46 is configured to record a noise generation record of a corresponding resident for each noise edge computing node, perform rule extraction on the record to obtain a noise generation rule, and send the noise generation rule to neighboring noise edge computing nodes;
the avoidance strategy module 47 is configured to predict the avoidance time of the neighboring noise edge calculation node according to the noise occurrence rule, and send a noise avoidance strategy to the corresponding resident.
Fig. 7 shows a constitution diagram of a building block according to an embodiment of the present invention. As can be seen from fig. 7, the build module includes:
an initial unit 411 for installing a plurality of noise edge calculation nodes at noise propagation outlets of each resident;
and the calibration unit 412 is configured to detect noise emitted by the specified resident at the same time by using the plurality of noise edge computing nodes, calibrate the noise according to all detection results, and determine the size and the azimuth of the noise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that various changes and substitutions are possible within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A community noise processing method based on edge computation, comprising:
noise edge computing nodes are arranged at noise transmission outlets of each resident in the community, the noise edge computing nodes are arranged facing the direction of a resident sound source, and the size and the direction of noise are collected through rotation;
when the collected noise exceeds a noise notification threshold, the size and the azimuth of a noise source are diffused to neighboring noise edge computing nodes through an edge computing network and are directly sent to a community manager;
when the collected noise exceeds a first noise control threshold, triggering a noise edge computing node corresponding to the noise source to execute a noise source control strategy, and sending a notification signal to a neighbor noise edge computing node, wherein the method comprises the following steps: according to the noise size, direction and distance of the noise source, sending notification signals according to different priorities, wherein the priorities are determined according to the following formula:
wherein P is the priority, DB is the noise size of the noise source, an is the included angle between the neighboring noise edge computing node and the noise source connecting line as well as the noise source noise propagation center line, dis is the distance between the neighboring noise edge computing node and the noise source;
the neighbor noise edge computing node continuously monitors the noise size and the direction after receiving the notification signal, and when the noise size exceeds a second noise control threshold value, the neighbor noise edge computing node is triggered to execute a noise transmission control strategy;
setting a noise frequency threshold, and when the frequency of the discrete noise source exceeds the noise frequency threshold, diffusing the size, frequency and direction of the noise source to neighboring noise edge computing nodes through an edge computing network and directly sending the noise source to a community manager;
each noise edge computing node records the noise generation record of the corresponding resident, and regularly extracts the record to obtain a noise generation rule, and sends the noise generation rule to the neighboring noise edge computing nodes;
and the neighbor noise edge computing node predicts the noise avoidance time according to the noise generation rule and sends a noise avoidance strategy to the corresponding resident.
2. The method of claim 1, wherein a noise edge calculation node is provided at a noise propagation outlet of each resident in the community, the noise edge calculation node being provided to face a direction of a sound source of the resident and collecting a size and a direction of noise by rotation, comprising:
a plurality of noise edge computing nodes are installed at the noise transmission outlets of each resident;
the noise edge computing nodes detect the noise emitted by the appointed householder at the same time, calibrate the noise according to all detection results and determine the size and the azimuth of the noise.
3. The method of claim 1, wherein said executing a noise source control strategy comprises:
sending reminding information to households corresponding to the noise sources;
releasing the sound deadening substance toward the noise source.
4. An edge computing-based community noise processing system employing the edge computing-based community noise processing method of any of claims 1-3, comprising:
the construction module is used for setting noise edge computing nodes at noise transmission outlets of each resident in the community, wherein the noise edge computing nodes are arranged facing the direction of the resident sound source and collect the size and the direction of noise through rotation;
the acquisition module is used for diffusing the size and the direction of the noise source to the neighboring noise edge computing nodes through the edge computing network when the acquired noise exceeds the noise notification threshold value, and directly transmitting the noise source to a community manager;
the first control module is used for triggering the noise edge computing node corresponding to the noise source to execute a noise source control strategy when the acquired noise exceeds a first noise control threshold value, and sending a notification signal to the neighbor noise edge computing node;
and the second control module is used for continuously monitoring the noise size and the direction after the neighbor noise edge computing node receives the notification signal, and triggering the neighbor noise edge computing node to execute a noise propagation control strategy when the noise size exceeds a second noise control threshold.
5. The system of claim 4, wherein the system further comprises:
the frequency detection module is used for setting a noise frequency threshold, and when the frequency of the discrete noise source exceeds the noise frequency threshold, the magnitude, the frequency and the direction of the noise source are diffused to the adjacent noise edge computing nodes through the edge computing network and are directly sent to a community manager.
6. The system of claim 4, wherein the system further comprises:
the rule extraction module is used for recording the noise generation record of the corresponding resident by each noise edge calculation node, carrying out rule extraction on the record to obtain a noise generation rule, and sending the noise generation rule to the adjacent noise edge calculation nodes;
and the avoidance strategy module is used for predicting the avoidance noise time according to the noise generation rule by the neighbor noise edge computing node and sending a noise avoidance strategy to the corresponding resident.
7. The system of claim 4, wherein the build module comprises:
an initial unit for installing a plurality of noise edge calculation nodes at noise propagation outlets of each resident;
and the calibration unit is used for simultaneously detecting the noise sent by the appointed householder by the plurality of noise edge calculation nodes, calibrating according to all detection results and determining the size and the azimuth of the noise.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911185800.4A CN111124667B (en) | 2019-11-27 | 2019-11-27 | Community noise processing method and system based on edge calculation |
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