CN113225539A - Floating population artificial intelligence early warning system based on cloud computing - Google Patents
Floating population artificial intelligence early warning system based on cloud computing Download PDFInfo
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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Abstract
The utility model provides a floating population artificial intelligence early warning system based on cloud belongs to artificial intelligence technical field, including high in the clouds server, thing networking module and a plurality of early warning police kiosk: the plurality of early warning kiosks are connected with the Internet of things module in a signal mode, and the Internet of things module is connected with the cloud server in a signal mode; wherein, early warning police kiosk includes: the data acquisition module is used for acquiring external data information to obtain training data; the data processing module is used for receiving the training data and preprocessing the training data; a database; the data processing module is used for storing the data processed by the data processing module and storing the data to different areas respectively; according to the invention, the plurality of early warning kiosks are arranged, so that multi-point coverage is realized, the diversification of the acquired data is ensured, the accuracy of subsequent data processing is improved, meanwhile, the cloud server and the Internet of things module are combined, the individual combat of the early warning kiosks is avoided, the resource sharing is realized, the data processing speed is improved, and the real-time performance of the system is ensured.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a floating population artificial intelligence early warning system and method based on cloud computing.
Background
The internet has emerged since 1960 and is mainly used for plain text e-mail or news cluster services between military parties, large enterprises and the like. The network has become one of the life necessities that people can not leave with the development of web sites and electronic commerce until 1990, which has begun to enter ordinary households. The concept of cloud computing is proposed for the first time in the search engine meeting of 8 months in 2006, and becomes a third revolution of the internet.
A small part of the floating population enters a city in a talent introduction mode, and the floating population has higher cultural level and more than all professional knowledge, so that the household registration problem is easy to solve and preferential policies of places can be enjoyed, but most of the foreign population has lower cultural level and lacks social security, and a large number of criminal cases are caused.
Disclosure of Invention
(1) Technical problem to be solved
The embodiment of the invention provides a floating population artificial intelligence early warning system based on cloud computing, which avoids the problem of a large number of criminal cases by arranging a plurality of early warning kiosks, an Internet of things module and a cloud server.
(2) Technical scheme
The embodiment of the invention provides a floating population artificial intelligence early warning system based on cloud computing, which comprises a cloud server, an Internet of things module and a plurality of early warning kiosks: the plurality of early warning kiosks are connected with the Internet of things module in a signal mode, and the Internet of things module is connected with the cloud server in a signal mode; wherein, early warning police kiosk includes:
the data acquisition module is used for acquiring external data information to obtain training data;
the data processing module is used for receiving the training data and preprocessing the training data;
a database; the data processing module is used for storing the data processed by the data processing module and storing the data to different areas respectively;
the data analysis module is used for analyzing the data stored in the database and discriminating the risk level; and the early warning module is used for carrying out early warning according to the risk level.
Further, the data processing module specifically classifies the following steps:
the method comprises the following steps: screening and collecting according to training data types, wherein the training data mainly comprises three categories of pictures, texts and sounds;
step two: and extracting characteristic values in the training data.
Further, the characteristic values in the second step include a person-specific constant a, a business activity number b, a vehicle-specific constant c, a negative language number d and a water-specific constant e.
Further, the data analysis module specifically works as follows:
s1: inputting the characteristic value into an algorithm model, wherein the algorithm model is firstly provided with a safety threshold F, and after the characteristic value is input into the algorithm model, outputting an event sum S ═ a + b + c + d + e, wherein a is more than or equal to 0, b is more than or equal to 0, c is more than or equal to 0, d is more than or equal to 0, and e is more than or equal to 0;
s2: comparing the relation between S and a safety threshold F;
s3: if S is less than F, continuously acquiring external data, and not starting the early warning module; and if S is larger than or equal to F, starting the early warning module, and meanwhile, calculating the probability of the event to determine the early warning level.
Further, in the event probability calculation in S3, the specific content of determining the early warning level is as follows: a Bayesian formula is adopted:
wherein, P (A) > 0, P (B)i) > 0, A is P (B) occurs if and only if an event in the complete event group occursiA) is the posterior probability, P (B)i) Is an event BiA is an observed value, P (A/B)i) Is BiProbability of observation under true value condition; according to P (B)iand/A), the early warning module starts a corresponding early warning level.
Further, the data acquisition module comprises a camera; the camera is internally provided with a WIFI module and a single chip microcomputer, the WIFI module is in signal connection with the single chip microcomputer, a serial port is fixedly arranged on the similar camera, and the serial port is in signal connection with the single chip microcomputer.
Further, the camera is in signal connection with the cloud server.
(3) Advantageous effects
In summary, the invention avoids the problem of occurrence of a large number of criminal cases by arranging the plurality of early warning kiosks, the internet of things module and the cloud server, wherein the plurality of early warning kiosks are arranged to realize multi-point coverage and ensure the diversification of collected data so as to improve the accuracy of subsequent data processing, and meanwhile, the cloud server and the internet of things module are combined to avoid individual combat of the early warning kiosks, realize resource sharing and improve the data processing speed, ensure the real-time performance of the system and accurately position the early warning level by combining the processing result, and avoid social resource waste.
Drawings
FIG. 1 is a schematic overall framework of the present invention;
FIG. 2 is a schematic diagram of a data acquisition module according to the present invention.
In the figure: 1-a cloud server; 2-an internet of things module; 3-early warning police kiosk; 4-a data acquisition module; 5-a data processing module; 6-a data analysis module; 7-early warning module; 8-a camera; 9-serial port; 10-a WIFI module; 11-a singlechip.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. The following detailed description of the embodiments and the accompanying drawings are provided to illustrate the principles of the invention and are not intended to limit the scope of the invention, i.e., the invention is not limited to the embodiments described, but covers any modifications, alterations, and improvements in the parts, components, and connections without departing from the spirit of the invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, the floating population artificial intelligence early warning system based on cloud computing comprises a cloud server 1, an internet of things module 2 and a plurality of early warning police kiosks 3: the plurality of early warning kiosks 3 are in signal connection with the internet of things module 2, and the internet of things module 2 is in signal connection with the cloud server 1; through setting up a plurality of early warning police box 3, realize the multiple spot and cover, ensure the diversification of data collection to improve follow-up data processing's accuracy, simultaneously, combine high in the clouds server 1 and thing networking module 2, avoid early warning police box 3's individual soldier to fight, realize resource sharing and improve data processing's speed, guarantee the real-time of system.
In this embodiment, the early warning kiosk 3 includes:
the data acquisition module 4 is used for acquiring external data information to obtain training data;
the data processing module 5 is used for receiving the training data, preprocessing the training data and then sending the preprocessed training data to the cloud server 1;
the data analysis module 6 is used for receiving the data sent by the cloud server for analysis and discriminating the risk level;
and the early warning module 7 is used for carrying out early warning according to the risk level.
It is worth mentioning that the early warning module 7 combines the processing result of the data analysis module 6 to accurately position the early warning level, thereby avoiding social resource waste.
The data processing module 5 specifically classifies the following steps:
the method comprises the following steps: screening and collecting according to training data types, wherein the training data mainly comprise pictures, texts and sounds, so that the diversity of the collected data is ensured, and the accuracy of subsequent links is improved;
step two: extracting a characteristic value in the training data, and transmitting the characteristic value to the cloud server 1; the function is that, through uploading to the high in the clouds server 1 and realizing the resource sharing, and can realize distributed computation through the high in the clouds server 1, improve system operation speed, wherein, it is worth noting that there are following:
(1) the cloud server 1 sends the characteristic values to the data analysis modules 6 in the plurality of early warning kiosks 3.
(2) The characteristic values comprise a person abnormal constant a, a business activity number b, a vehicle abnormal constant c, a negative explanation line number d and a water and electricity abnormal constant e.
The data analysis module 6 specifically works as follows:
s1: inputting the characteristic value into an algorithm model, wherein the algorithm model is firstly provided with a safety threshold F, and after the characteristic value is input into the algorithm model, outputting an event sum S ═ a + b + c + d + e, wherein a is more than or equal to 0, b is more than or equal to 0, c is more than or equal to 0, d is more than or equal to 0, and e is more than or equal to 0;
s2: comparing the relation between S and a safety threshold F;
s3: if S is less than F, continuing to acquire external data, and not starting the early warning module 7; if S is larger than or equal to F, starting the early warning module 7, and meanwhile, performing event probability calculation to determine an early warning level; the method comprises the following specific steps:
a Bayesian formula is adopted:
wherein, P (A) > 0, P (B)i) > 0, A is P (B) occurs if and only if an event in the complete event group occursiA) is the posterior probability, P (B)i) Is an event BiA is an observed value, P (A/B)i) Is BiProbability of observation under true value condition; according to P (B)iThe calculated value of/A), the early warning module 7 starts the corresponding early warning level, so that the user can take adaptive measures for prevention.
As can be seen in fig. 2, the data acquisition module 4 includes a camera 8; camera 8 embeds there are WIFI module 10 and singlechip 11, in this embodiment, WIFI module 10 adopts E103-W01 module, singlechip 11 model is 80C51, wherein, WIFI module 10 and 11 signal connection of singlechip, and set firmly serial ports 9 on the similar camera 8 for text information is carried to the manual work, increases the pluralism of data acquisition type, thereby ensures the precision of early warning grade and early warning effect, secondly, serial ports 9 with 11 signal connection of singlechip.
It should be noted that the camera 8 is fixedly arranged on the early warning kiosk 3, the camera 8 is in signal connection with the data processing module 5, and the camera 8 is in signal connection with the cloud server 1.
The working principle of the invention is as follows: a plurality of early warning kiosks 3 are distributed at a mounting point, and environmental data and behavior data around the early warning kiosks 3 are monitored in an all-around manner by cameras 8 for twenty-four hours, wherein the environmental data comprises surrounding building data, weather data, road surface conditions and other data; behavioral data, i.e., activity data related to a person, such as: this regional operation activity data, data such as vehicle traffic data and speech action, and simultaneously, the user can carry some artifical data of gathering to data acquisition module 4 through serial ports 9 or WIFI module 10, then, upload respectively to high in the clouds server 1 branch item storage with foretell data according to the type through data processing module 5, and simultaneously, extract the eigenvalue, proofread through data analysis module 6, whether need carry out the rank of early warning and early warning, the user of being convenient for in time knows the condition, be favorable to maintaining regional stability and safety.
In the description of the present invention, it is to be understood that the terms "central," "lateral," "upper," "lower," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the indicated orientations and positional relationships, based on the orientations and positional relationships illustrated in the drawings, to facilitate the description of the invention and to simplify the description, but do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the invention. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified. Furthermore, the term "comprises" and any variations thereof is intended to cover non-exclusive inclusions.
The present invention has been described in terms of embodiments, and several variations and modifications can be made to the device without departing from the principles of the present invention. It should be noted that all the technical solutions obtained by means of equivalent substitution or equivalent transformation, etc., fall within the protection scope of the present invention.
Claims (8)
1. The utility model provides a floating population artificial intelligence early warning system based on cloud calculates which characterized in that: including high in the clouds server (1), thing networking module (2) and a plurality of early warning police kiosk (3): the plurality of early warning kiosks (3) are in signal connection with the Internet of things module (2), and the Internet of things module (2) is in signal connection with the cloud server (1); wherein, early warning police kiosk (3) includes:
the data acquisition module (4) is used for acquiring external data information to obtain training data;
the data processing module (5) is used for receiving the training data, preprocessing the training data and then sending the training data to the cloud server (1);
the data analysis module (6) is used for receiving the data sent by the cloud server for analysis and discriminating the risk level;
and the early warning module (7) carries out early warning according to the risk level.
2. The floating population artificial intelligence early warning system based on cloud computing of claim 1, characterized in that: the data processing module (5) comprises the following specific classification steps:
the method comprises the following steps: screening and collecting according to training data types, wherein the training data mainly comprises three categories of pictures, texts and sounds;
step two: and extracting the characteristic values in the training data and transmitting the characteristic values to the cloud server (1).
3. The floating population artificial intelligence early warning system based on cloud computing according to claim 2, wherein: the cloud server (1) sends the characteristic values to a plurality of data analysis modules (6) in the early warning police kiosks (3).
4. The floating population artificial intelligence early warning system based on cloud computing according to claim 2, wherein: and in the second step, the characteristic values comprise a personnel abnormity constant a, a business activity number b, a vehicle abnormity constant c, a negative explanation line number d and a water abnormity constant e.
5. The floating population artificial intelligence early warning system based on cloud computing of claim 1, characterized in that: the data analysis module (6) specifically works as follows:
s1: inputting the characteristic value into an algorithm model, wherein the algorithm model is firstly provided with a safety threshold F, and after the characteristic value is input into the algorithm model, outputting an event sum S ═ a + b + c + d + e, wherein a is more than or equal to 0, b is more than or equal to 0, c is more than or equal to 0, d is more than or equal to 0, and e is more than or equal to 0;
s2: comparing the relation between S and a safety threshold F;
s3: if S is less than F, continuously acquiring external data, and not starting the early warning module (7); and if S is larger than or equal to F, starting an early warning module (7), and meanwhile, performing event probability calculation to determine an early warning level.
6. The floating population artificial intelligence early warning system based on cloud computing according to claim 4, wherein: in S3, event probability calculation, and the specific content of determining the early warning level is as follows: a Bayesian formula is adopted:
wherein, P (A) > 0, P (B)i) > 0, A is P (B) occurs if and only if an event in the complete event group occursiA) is the posterior probability, P (B)i) Is an event BiA is an observed value, P (A/B)i) Is BiProbability of observation under true value condition; according to P (B)iThe calculated value of/A), and the early warning module (7) starts the corresponding early warning level.
7. The floating population artificial intelligence early warning system based on cloud computing of claim 1, characterized in that: the data acquisition module (4) comprises a camera (8); camera (8) embeds has WIFI module (10) and singlechip (11), WIFI module (10) and singlechip (11) signal connection, and has set firmly serial ports (9) on similar camera (8), serial ports (9) with singlechip (11) signal connection.
8. The floating population artificial intelligence early warning system based on cloud computing of claim 7, characterized in that: the camera (8) is in signal connection with the cloud server (1).
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Cited By (1)
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