CN115174608A - Smart city security monitoring system based on Internet of things - Google Patents
Smart city security monitoring system based on Internet of things Download PDFInfo
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- CN115174608A CN115174608A CN202210577204.6A CN202210577204A CN115174608A CN 115174608 A CN115174608 A CN 115174608A CN 202210577204 A CN202210577204 A CN 202210577204A CN 115174608 A CN115174608 A CN 115174608A
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Abstract
The invention relates to a city monitoring system, in particular to a smart city security monitoring system based on the Internet of things, which comprises a server, wherein the server acquires flowing target characteristic quantity reflecting the influence degree of a flowing target on flowing individuals through a flowing target characteristic quantity acquisition module, and obtains the flowing tendency of each flowing individual on the corresponding flowing target through a flowing individual tendency analysis module based on flowing target characteristic quantity analysis; the technical scheme provided by the invention can effectively overcome the defects that monitoring equipment cannot be reasonably and effectively installed aiming at the mobility of people and the communication data cannot be efficiently analyzed and processed in the prior art.
Description
Technical Field
The invention relates to a city monitoring system, in particular to an intelligent city security monitoring system based on the Internet of things.
Background
The smart city is a new city development mode integrating city development planning, city operation management, city economic and social development, new generation information technology application and the like, is a necessary choice for promoting city scientific development, crossover development and harmonious development, and is an important guarantee for improving the comprehensive competitiveness and international influence of modern cities.
The smart city senses, analyzes and integrates various key information of a city operation core system by using information and communication technical means, so that the smart city can intelligently respond to various requirements including civil, environmental, public safety, urban service and industrial and commercial activities. The essence of the smart city is that the smart management and operation of the city are realized by using advanced information technology, so that a better life is created for people in the city, and the harmonious and sustainable growth of the city is promoted.
City monitoring is a very important link in smart cities, and specific conditions in the cities, such as people flow information, traffic flow information and the like, are acquired through a monitoring system, so that city management workers can know the specific conditions at a far end in real time, and can provide timely city road condition information for citizens.
However, how to reasonably and effectively install monitoring equipment aiming at the mobility of people mainly depends on the design experience of a construction party for evaluation at present, and an effective analysis method is lacked. In addition, because the amount of urban traffic data is huge, a large amount of data can be generated at the same time, how to set the priority of the traffic data is realized, and the problem to be solved is also how to realize efficient data analysis and processing.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides the smart city security monitoring system based on the Internet of things, which can effectively overcome the defects that monitoring equipment cannot be reasonably and effectively installed according to the mobility of people and high-efficiency data analysis and processing cannot be performed on general data in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a smart city security monitoring system based on the Internet of things comprises a server, wherein the server acquires flowing target characteristic quantities reflecting the influence degree of flowing targets on flowing individuals through a flowing target characteristic quantity acquisition module, and obtains flowing tendency degrees of each flowing individual on corresponding flowing targets through a flowing individual tendency analysis module based on flowing target characteristic quantity analysis;
the server receives monitoring data uploaded by the monitoring clusters through the monitoring data receiving module, corresponding data analysis models are selected by the monitoring data analysis module to analyze the received monitoring data, the server generates common data packets and urgent data packets based on data analysis results through the data packet generation module, uploads the data packets to a plurality of data centers through the data packet uploading module, and receives processing results of the data packets sent by the data centers through the processing result receiving module and broadcasts the processing results to the client.
Preferably, the flowing target characteristic quantity obtaining module selects a starting position of the flowing individual from the urban facility and establishes a flowing target characteristic quantity of the flowing target to the flowing individual.
Preferably, the flow target characteristic quantity is expressed as:
f 1 (d 1 )*ω 1 ，f 2 (d 2 )*ω 2 ，...，f i (d i )*ω i
wherein, i = [1, k =]K is the number of targets flowing in the set area, f i (d i ) For the influence factor of the ith flow target on the flowing individual, d i Is the distance of the path between the ith mobile target and the mobile individual, and the influence factor f i (d i ) Distance d from path i Is in inverse proportion;
ω i the influence coefficient, the influence coefficient omega, of the ith flowing target i Proportional to the size of the flow target and the type of function it has.
Preferably, the flowing individual tendency analysis module inputs the flowing target characteristic quantity into the corresponding flowing individual behavioral neural network model according to the condition of the flowing individual to obtain the flowing tendency degree of the flowing individual to the corresponding flowing target.
Preferably, the flow target screening module screens out a centralized flow target based on a flow individual number threshold value and a flow individual number ranking;
the monitoring layout scheme generation module determines the number of installed monitoring devices according to the number of the corresponding flowing individuals attracted by the concentrated flowing targets, and determines each installation area in the urban security monitoring layout scheme and the distribution condition of the monitoring devices in each installation area by combining area map information.
Preferably, each monitoring cluster performs ad hoc networking according to network performance, constructs a multi-level redundant network, and selects a data uploading mode according to the type of the collected data;
when the multilevel redundant network is adopted to upload data, the network path of each network is selected according to the network structure of the multilevel redundant network, so that the data can be uploaded to the server in the shortest time.
Preferably, the data analysis module selects a corresponding data analysis model to analyze the received monitoring data, obtains traffic data including a light index α, a current people flow density ρ, a flow λ of the non-motor vehicle, and a motor vehicle flow γ, and calculates a weight ω:
preferably, the data packet generation module calculates (ρ + λ)/γ when the vehicle flow γ is greater than a first threshold; and if the (rho + lambda)/gamma is larger than a second threshold value, packing the traffic data together with the corresponding weight omega to generate an urgent data packet, and otherwise, generating a common data packet.
Preferably, the system further comprises a data transmission time slot receiving module, the server receives the transmission time slot and the emergency time slot allocated to each server by the data center through the data transmission time slot receiving module, and the data center determines the ratio of the transmission time slots of each server based on the average value of the weights ω corresponding to the historical traffic data of each server.
Preferably, when the data packet uploading module receives an urgent data packet sent by the data packet generating module, the emergency degree is determined according to the weight ω, the number of the uploaded data centers is determined according to the emergency degree, then an emergency time slot is selected, a network is accessed by a contention mechanism, and the urgent data packet is uploaded to the corresponding data center.
(III) advantageous effects
Compared with the prior art, the smart city security monitoring system based on the Internet of things has the following beneficial effects:
1) The mobile target characteristic quantity obtaining module obtains mobile target characteristic quantity reflecting the influence degree of mobile targets on the mobile individuals, the mobile individual tendency analyzing module obtains the mobile tendency of each mobile individual to the corresponding mobile target based on the mobile target characteristic quantity analysis, the mobile individual distribution predicting module performs distribution prediction on the number of the mobile individuals attracted by each mobile target based on the mobile tendency, the mobile target screening module screens out concentrated mobile targets, and the monitoring layout scheme generating module generates an urban security monitoring layout scheme based on the concentrated mobile targets, so that monitoring equipment can be reasonably and effectively installed aiming at the mobility of people in cities, the monitoring equipment is enabled to be distributed in a people group activity area in the cities, and the resource utilization rate is effectively improved;
2) The monitoring data receiving module receives monitoring data uploaded by the monitoring cluster, the monitoring data analysis module selects a corresponding data analysis model to analyze the received monitoring data, and the data packet generation module generates a common data packet and an urgent data packet based on a data analysis result, so that a priority division mechanism for urban traffic data is established, the urban traffic data is subjected to efficient data analysis and processing, and citizens can know urban road condition information in time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic diagram of the system of the present invention;
fig. 2 is a schematic flow chart of data analysis processing performed on traffic data according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
A smart city security monitoring system based on the Internet of things is disclosed, as shown in figure 1, and comprises a server, wherein the server acquires flowing target characteristic quantity reflecting the influence degree of a flowing target on flowing individuals through a flowing target characteristic quantity acquisition module, and obtains the flowing tendency degree of each flowing individual on the corresponding flowing target through a flowing individual tendency analysis module based on flowing target characteristic quantity analysis, the server conducts distribution prediction on the number of the flowing individual attracted by each flowing target through a flowing individual distribution prediction module based on the flowing tendency degree, and screens out centralized flowing targets through a flowing target screening module, and the server generates a city security monitoring layout scheme through a monitoring layout scheme generation module based on the centralized flowing target.
The mobile target characteristic quantity acquisition module selects a mobile individual starting position from urban facilities (including transportation facilities, medical facilities, educational facilities, residential facilities, entertainment facilities and the like) and establishes a mobile target characteristic quantity of a mobile target to the mobile individual.
In the technical solution of the present application, the flow target feature quantity is expressed as:
f 1 (d 1 )*ω 1 ，f 2 (d 2 )*ω 2 ，...，f i (d i )*ω i
wherein i = [1, k ]]K is the number of targets flowing in the set area, f i (d i ) For the influence factor of the ith flow target on the flowing individual, d i Is the distance of the path between the ith mobile target and the mobile individual, and the influence factor f i (d i ) Distance d from path i Is in inverse proportion;
ω i the influence coefficient, the influence coefficient omega, of the ith flowing target i Proportional to the size of the flow target and the type of function it has.
And the flowing individual tendency analysis module inputs the flowing target characteristic quantity into the corresponding flowing individual behavior neural network model according to the condition of the flowing individual to obtain the flowing tendency degree of the flowing individual to the corresponding flowing target.
The flow target screening module screens out the centralized flow targets based on the flow individual quantity threshold value and the flow individual quantity ranking.
The monitoring layout scheme generation module determines the number of installed monitoring devices according to the number of the corresponding flowing individuals attracted by the concentrated flowing targets, and determines each installation area in the urban security monitoring layout scheme and the distribution condition of the monitoring devices in each installation area by combining with the area map information.
In the technical scheme of the application, the reasonable and effective process for installing the monitoring equipment aiming at the mobility of the crowd is briefly described as follows:
a flow target characteristic quantity acquisition module acquires a flow target characteristic quantity;
the flow individual tendency analysis module obtains the flow tendency degree of each flow individual to the corresponding flow target based on the flow target characteristic quantity analysis;
the flowing individual distribution prediction module performs distribution prediction on the number of the flowing target attracted flowing individuals on the basis of the flowing tendency degree;
the flow target screening module screens out a centralized flow target;
and the monitoring layout scheme generating module generates an urban security monitoring layout scheme based on the centralized flow target. Through the process, the monitoring equipment can be reasonably and effectively installed aiming at the mobility of people in the city, so that the monitoring equipment is distributed in the people cluster activity area in the city, and the resource utilization rate is effectively improved.
As shown in fig. 1 and 2, the server receives monitoring data uploaded by the monitoring cluster through the monitoring data receiving module, and selects a corresponding data analysis model to analyze the received monitoring data by using the monitoring data analysis module, the server generates a normal data packet and an urgent data packet based on a data analysis result through the data packet generation module, and uploads the data packets to a plurality of data centers through the data packet uploading module, and the server receives a processing result of the data packets sent by the data centers through the processing result receiving module and broadcasts the processing result to the client.
In the technical scheme, each monitoring cluster performs ad hoc network according to network performance, a multi-level redundant network is constructed, and a data uploading mode is selected according to the type of the acquired data. When the multi-level redundant network is adopted to upload data, the network path of each network is selected according to the network structure of the multi-level redundant network, so that the data can be uploaded to the server in the shortest time.
The data analysis module selects a corresponding data analysis model to analyze the received monitoring data, obtains traffic data including a light index alpha, a current pedestrian flow density rho, a non-motor vehicle flow lambda and a motor vehicle flow gamma, and calculates a weight omega:
the data packet generation module calculates (rho + lambda)/gamma when the flow gamma of the motor vehicle is larger than a first threshold value; and if the (rho + lambda)/gamma is larger than a second threshold value, packing the traffic data together with the corresponding weight omega to generate an urgent data packet, and otherwise, generating a common data packet.
According to the technical scheme, the system further comprises a data transmission time slot receiving module, the server receives the transmission time slots and the emergency time slots distributed by the data center for each server through the data transmission time slot receiving module, and the data center determines the ratio of the transmission time slots of each server based on the average value of the weights omega corresponding to the historical traffic data of each server.
And when the data packet uploading module receives the urgent data packet sent by the data packet generating module, determining the emergency degree according to the weight omega, determining the number of uploaded data centers according to the emergency degree, then selecting an emergency time slot, accessing the network by a competition mechanism, and uploading the urgent data packet to the corresponding data center.
According to the technical scheme, the monitoring data receiving module receives monitoring data uploaded by the monitoring cluster, the monitoring data analysis module selects a corresponding data analysis model to analyze the received monitoring data, and the data packet generation module generates a common data packet and an urgent data packet based on a data analysis result, so that a priority division mechanism for urban traffic data is established, the urban traffic data is subjected to efficient data analysis and processing, and citizens can know urban road condition information in time.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. The utility model provides a wisdom city security protection monitored control system based on thing networking which characterized in that: the system comprises a server, wherein the server acquires flowing target characteristic quantity reflecting the influence degree of a flowing target on flowing individuals through a flowing target characteristic quantity acquisition module, and obtains the flowing tendency of each flowing individual on the corresponding flowing target through a flowing individual tendency analysis module based on flowing target characteristic quantity analysis;
the server receives monitoring data uploaded by the monitoring clusters through the monitoring data receiving module, corresponding data analysis models are selected by the monitoring data analysis module to analyze the received monitoring data, the server generates common data packets and urgent data packets based on data analysis results through the data packet generation module, uploads the data packets to a plurality of data centers through the data packet uploading module, and receives processing results of the data packets sent by the data centers through the processing result receiving module and broadcasts the processing results to the client.
2. The smart city security monitoring system based on the internet of things as claimed in claim 1, wherein: the flowing target characteristic quantity obtaining module selects the initial position of the flowing individual from the urban facility and establishes the flowing target characteristic quantity of the flowing individual by the flowing target.
3. The smart city security monitoring system based on the internet of things as claimed in claim 2, wherein: the flow target characteristic quantity is expressed as:
f 1 (d 1 )*ω 1 ，f 2 (d 2 )*ω 2 ，...，f i (d i )*ω i
wherein, i = [1, k =]K is the number of flow targets in the set area, f i (d i ) For the influence factor of the ith flow target on the flowing individual, d i Is the distance of the path between the ith mobile object and the mobile individual, and the influence factor f i (d i ) Distance d from path i Is in inverse proportion;
ω i the influence coefficient, namely the influence coefficient omega of the ith flowing target i Proportional to the size of the flow target and the type of function it has.
4. The smart city security monitoring system based on the internet of things as claimed in claim 1, wherein: and the flowing individual tendency analysis module inputs the flowing target characteristic quantity into the corresponding flowing individual behavior neural network model according to the condition of the flowing individual to obtain the flowing tendency degree of the flowing individual to the corresponding flowing target.
5. The smart city security monitoring system based on the internet of things as claimed in claim 1, wherein: the mobile target screening module screens out centralized mobile targets on the basis of a mobile individual quantity threshold and mobile individual quantity ranking;
the monitoring layout scheme generation module determines the number of installed monitoring equipment according to the number of the corresponding flowing individuals attracted by the centralized flowing targets, and determines each installation area in the urban security monitoring layout scheme and the distribution condition of the monitoring equipment in each installation area by combining with area map information.
6. The smart city security monitoring system based on the internet of things as claimed in claim 1, wherein: each monitoring cluster performs ad hoc network according to network performance, establishes a multi-level redundant network, and selects a data uploading mode according to the type of the acquired data;
when the multi-level redundant network is adopted to upload data, the network path of each network is selected according to the network structure of the multi-level redundant network, so that the data can be uploaded to the server in the shortest time.
7. The smart city security monitoring system based on the internet of things as claimed in claim 1, wherein: the data analysis module selects a corresponding data analysis model to analyze the received monitoring data, obtains traffic data including a light index alpha, a current pedestrian flow density rho, a non-motor vehicle flow lambda and a motor vehicle flow gamma, and calculates a weight omega:
8. the smart city security monitoring system based on the internet of things as claimed in claim 7, wherein: the data packet generation module calculates (rho + lambda)/gamma when the flow gamma of the motor vehicle is larger than a first threshold value; and if the (rho + lambda)/gamma is larger than a second threshold value, packing the traffic data together with the corresponding weight omega to generate an urgent data packet, and otherwise, generating a common data packet.
9. The smart city security monitoring system based on the internet of things as claimed in claim 1, wherein: the server receives the transmission time slot and the emergency time slot which are distributed to each server by the data center through the data transmission time slot receiving module, and the data center determines the ratio of the transmission time slots of the servers based on the average value of the weights omega corresponding to the historical traffic data of the servers.
10. The internet of things-based smart city security monitoring system according to claim 9, wherein: and when the data packet uploading module receives the urgent data packet sent by the data packet generating module, determining the emergency degree according to the weight omega, determining the number of uploaded data centers according to the emergency degree, then selecting an emergency time slot, accessing the network by a competition mechanism, and uploading the urgent data packet to the corresponding data center.
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| CN202210577204.6A CN115174608A (en) | 2022-05-25 | 2022-05-25 | Smart city security monitoring system based on Internet of things |
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| WO2020211430A1 (en) * | 2019-04-16 | 2020-10-22 | 广东康云科技有限公司 | Smart city system and implementation method therefor |
| CN111835873A (en) * | 2020-09-17 | 2020-10-27 | 杭州博采网络科技股份有限公司 | Smart city big data analysis and monitoring system |
| CN113271435A (en) * | 2021-03-26 | 2021-08-17 | 邓立 | Smart city safety monitoring system |
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- 2022-05-25 CN CN202210577204.6A patent/CN115174608A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107146424A (en) * | 2017-07-06 | 2017-09-08 | 安徽超清科技股份有限公司 | A kind of smart city pedestrian indicates system |
| WO2020211430A1 (en) * | 2019-04-16 | 2020-10-22 | 广东康云科技有限公司 | Smart city system and implementation method therefor |
| CN110309953A (en) * | 2019-05-28 | 2019-10-08 | 特斯联（北京）科技有限公司 | Using the city safety monitoring layout system and method for object mobility forecast of distribution |
| CN111835873A (en) * | 2020-09-17 | 2020-10-27 | 杭州博采网络科技股份有限公司 | Smart city big data analysis and monitoring system |
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