CN111328021B - Superbusiness scene early warning method and system for Internet of things prevention and control - Google Patents
Superbusiness scene early warning method and system for Internet of things prevention and control Download PDFInfo
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Abstract
The embodiment of the invention provides a super-service scene early warning method and a system for prevention and control of the Internet of things, wherein the method comprises the following steps: defining a service scene of the user of the Internet of things according to historical user data of the user of the Internet of things, and obtaining service scene information of the user of the Internet of things; obtaining service use behavior information of the user of the Internet of things, and calculating the service displacement attribute of the user of the Internet of things according to the service use behavior information and the service scene information; if the service displacement attribute of the user of the Internet of things meets the preset condition, judging that the user of the Internet of things is in a super-service scene state; the service scene information at least comprises one item or a combination of a plurality of items of user behavior scene information, user position scene information and user flow scene information. The method provided by the embodiment of the invention fully utilizes the data characteristic analysis and calculation to carry out deep analysis on the user behavior, and further defines the business scene rule according to the analysis result.
Description
Technical Field
The embodiment of the invention relates to the field of mobile services, in particular to a super-service scene early warning method and system for prevention and control of the Internet of things.
Background
Under the situation of explosive development of the business of the internet of things, the using scene of the user of the internet of things is refined and classified, whether the behavior track of the user is matched with the using scene or not can be monitored in real time, the real application of the user can be mastered, risk users such as signing information false and secondary resale can be identified, the intelligent prevention and control capacity of business risks is improved, and the assistance is provided for healthy development of the business of the internet of things.
At present, a professional method is generally adopted to define a service scene of the internet of things, agree on the service scene when a card is opened, monitor whether the use behavior of a user exceeds the agreed service scene or not on the basis of a signing service scene, and perform early warning on the user exceeding the service scene.
In the prior art, the expert familiarity and analysis capability of the service are relied on, subjective factors and artificial factors are excessively relied on, so that the method has great limitation, and the defined service scene is possibly not in accordance with the actual situation, so that the mining of risk users is influenced, and the service risk prevention and control capability is reduced.
Disclosure of Invention
The embodiment of the invention provides an Internet of things prevention and control oriented super-service scene early warning method and system, which are used for solving the problems that in the prior art, the super-service scene early warning method and system excessively depend on subjective factors and artificial factors, so that the super-service scene has great limitation, and a defined service scene is possibly not in line with the actual situation, so that the excavation of risk users is influenced, and the service risk prevention and control capacity is reduced.
In a first aspect, an embodiment of the present invention provides a super-service scene early warning method for prevention and control of an internet of things, including: defining a service scene of an Internet of things user according to historical user data of the Internet of things user to obtain service scene information of the Internet of things user;
obtaining service use behavior information of the user of the Internet of things, and calculating a service displacement attribute of the user of the Internet of things according to the service use behavior information and the service scene information;
if the service displacement attribute of the user of the Internet of things meets a preset condition, judging that the user of the Internet of things is in a super-service scene state;
the service scene information at least comprises one or more of user behavior scene information, user position scene information and user flow scene information.
In a second aspect, an embodiment of the present invention provides a super-service scene early warning system for prevention and control of an internet of things, including:
the service scene definition module is used for defining the service scene of the user of the Internet of things according to the historical user data of the user of the Internet of things to obtain the service scene information of the user of the Internet of things;
the service scene calculation module is used for obtaining service use behavior information of the Internet of things user and calculating the service displacement attribute of the Internet of things user according to the service use behavior information and the service scene information;
the early warning module is used for judging that the user of the Internet of things is in a super-service scene state if the service displacement attribute of the user of the Internet of things meets a preset condition;
the service scene information at least comprises one or more of user behavior scene information, user position scene information and user flow scene information.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program that is stored in the memory and is executable on the processor, where the processor implements the steps of the foregoing method for early warning of a super-service scene for prevention and control of an internet of things when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for early warning a super-service scene facing prevention and control of an internet of things as provided in the first aspect.
According to the super-service scene early warning method and system for prevention and control of the Internet of things, a super-service scene recognition model is established based on service scene information, early warning is conducted on a user who recognizes a super-service scene, deep analysis is conducted on user behaviors by means of data characteristic analysis and calculation, and then a service scene rule is defined according to an analysis result.
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 description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a super-service scene early warning method for prevention and control of the internet of things according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a super-service scene early warning system for prevention and control of the internet of things according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of 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, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a super-service scene early warning method for prevention and control of the internet of things according to an embodiment of the present invention, where the method includes:
s1, defining the service scene of the user of the Internet of things according to the historical user data of the user of the Internet of things, and obtaining the service scene information of the user of the Internet of things.
S2, obtaining the service use behavior information of the user of the Internet of things, and calculating the service displacement attribute of the user of the Internet of things according to the service use behavior information and the service scene information.
S3, if the service displacement attribute of the Internet of things user meets the preset condition, judging that the Internet of things user is in a super-service scene state.
The service scene information at least comprises one or more of user behavior scene information, user position scene information and user flow scene information.
Specifically, in this embodiment, the service scenario of each internet of things user is defined one by one according to the historical data of the internet of things user, and the service scenario information that needs to be defined includes whether a voice/short message/traffic function can be used, whether displacement is allowed, and a reasonable range of traffic usage. In the scene definition process, the service function definition, the displacement determination and the flow range definition can be divided, for example, a certain internet of things user can determine that the service scene information of the user is specifically: the flow and short message functions can be opened, the voice function cannot be opened, displacement can be generated, and the flow range is 1G-5G.
Generally, the same service scenario identifier may correspond to the same service scenario; different service scene identifications correspond to different service scenes. According to the service scene identification selected by the user, the users selecting the same service scene identification can be divided into the user groups under the service scene corresponding to the identification. And further collecting the behavior data of the user group for analysis.
After the service scene definition is carried out on the user of the Internet of things, the service use behavior information of the user of the Internet of things in one month is analyzed, so that the service displacement attribute of the user of the Internet of things in the service use process is judged, specifically, the behavior data of the user group of the Internet of things stock is obtained and expressed by a user, the user comprises two parts of data, one part of the data is user data ub, and the user data ub comprises a card number cardNo, a service scene scenario, a month voice call duration call, a month short message number sms and a month use flow (M) gprs; the second part is user base station data us which comprises a card number, a base station cell code, a base station cell longitude, a base station cell latitude, an antenna angle, an azimuth angle, a frequency and an altitude. On the other hand, whether each user of the internet of things is displaced in the current month is calculated according to the us partial data, and result data uw is obtained through a displacement judgment algorithm and comprises a card number cardNo and whether displacement occurs in the current month, 1 represents displacement, and 0: indicating that no displacement has occurred.
For any of the internet of things users ub [ i ]. Taking out corresponding service scene data bs according to ub [ i ]. scenario, wherein the service scene data bs comprises a service scene name scenario, whether a voice isCall can be opened, whether a short message isSms can be opened, whether a flow isGprs can be opened, whether an isDisplace can be displaced, a minimum flow (M) minGprs and a maximum flow (M) maxGprs; and taking out corresponding displacement result judgment data ud from uw according to ub [ i ] and cardNO. And if one of the following conditions is met, determining the scene is a super-service scene:
ub [ i ]. call >0, and bs.iscall ═ 0; represents: the service scene corresponding to the user does not allow the voice to be opened, and the user has the voice call duration.
ub [ i ] sms >0, and bs.issms ═ 0; represents: the service scene corresponding to the user does not allow the opening of the short message, and the number of the short messages in the month of the user is more than 0 (the short messages are used).
ub [ i ] gprs >0, and bs.isgprs ═ 0; represents: the service scene corresponding to the user does not allow the flow to be opened, and the flow of the user in the month is larger than 0M (the flow is used).
ub [ i ] gprs > bs.maxgprs; represents: the current monthly flow usage of the user exceeds the maximum flow of the service scenario.
ub [ i ] gprs < bs.mingprs; represents: the current monthly flow usage of the user is lower than the minimum flow of the service scene.
D. display >0, and bs.isplay ═ 0; represents: the service scene corresponding to the user does not allow displacement use, and the user appears displacement use behavior in the current month.
And then, early warning is carried out on the identified super-service scene users, wherein the early warning content comprises card numbers, signed service scenes, super-service scene types (service function super-scenes, displacement super-scenes, flow exceeding ranges and flow lower ranges) and super-service scene content (service function names, displacement attributes and using flows).
By the method, a super-service scene recognition model is established based on service scene information, early warning is carried out on the user who recognizes the super-service scene, deep analysis is carried out on the user behavior by fully utilizing data characteristic analysis and calculation, and then a service scene rule is defined according to an analysis result.
On the basis of the above embodiment, the step of defining the service scenario of the internet of things user according to the historical user data of the internet of things user and obtaining the service scenario information of the internet of things user specifically includes:
acquiring basic data of an internet of things user, wherein the basic data at least comprises: card number, signing service scene name, city, business department, group unit name, group unit BOSS code, and one or more data combinations in industry type; acquiring behavior data of an Internet of things user, wherein the behavior data at least comprises the monthly mobile service use information data of the Internet of things user and base station coding information; and taking the basic data and the behavior data as historical user data of the Internet of things user, and defining the service scene of the Internet of things user based on the twenty-eight law.
The step of defining the service scene of the internet of things user specifically comprises the following steps: and judging that more than 80% of users of the Internet of things do not use the first service within a preset time range according to the historical user data, and judging that the first service does not need to be opened in the service scene.
The step of defining the service scene of the internet of things user based on the twenty-eight law further comprises the following steps: calculating a coverage sector of a base station by using signal attenuation, and calculating the position movement of the user of the Internet of things by using a triangulation positioning principle; and judging that more than 80% of users of the Internet of things do not have position movement within a preset time range, and judging that the users of the Internet of things do not have position movement in the service scene.
Specifically, considering that the signing of the business scenario has human subjectivity, and it is highly likely that the signed business scenario does not conform to the actual user usage scenario, based on the 80/20 rule, the business function rule of the business scenario is defined: u represents the behavior data of the user group, including card number, service scene, the time length of the voice call in the month, the number of short messages in the month and the using flow in the month; UA represents the number of users in the user group; UY represents the number of users in the user group with the current month voice call duration being more than 0; UD represents the number of users with the number of short messages in the current month being more than 0 in the user group; UL indicates the number of users in the user group who use traffic >0 in the current month. Sequentially taking out U1, U2, … … U n from user group U, if the time length of voice call in the month is greater than 0, then UY + 1; if the number of short messages in the current month is greater than 0, UD + 1; if its monthly usage traffic is >0, UL + 1.
If (UA-UY)/UA > -80%: the voice function is not used by 80% of users in the service scene in the month, so that the service scene can be judged not to need to be activated. If (UA-UD)/UA > -80%: the short message function is not used by 80% of users in the service scene in the month, so that the service scene can be judged not to need to be opened. If (UA-UL)/UA > -80%: the service scene indicates that 80% of users do not use the flow function in the month, so that the service scene can be judged not to need to open the flow function.
For the position information of the user of the Internet of things, 1) analyzing whether the user generates displacement in the current month by judging whether the sectors of the base stations are overlapped or not by utilizing a GIS (geographic information system) technology according to the base station cell information corresponding to the user base station ST, and if the sectors of any two base stations do not have overlapped areas, judging that the user generates displacement in the current month. The method can be realized by the following steps:
step one, drawing a coverage sector of each base station. Acquiring base station cell parameters (base station cell code, base station code, longitude lat, latitude lon, antenna height h, direction angle d, frequency f and antenna angle a) corresponding to a base station, drawing coverage sectors (one base station cell corresponds to one coverage sector) of each base station according to loss by using a COST-231Hata simulation model in a mobile wireless channel, wherein the algorithms of an indoor base station and an outdoor base station are different.
The outdoor base station sector algorithm can be divided into the following steps, firstly, the effective coverage radius is calculated according to the loss, the outdoor base station loss is 103, and the height of the mobile station is 1.5 meters according to the communication standard.
Calculating the coverage radius according to the loss, the frequency f, the antenna height h, the mobile station height and the base station type, wherein the specific algorithm is as follows (sr represents the coverage radius of the base station):
when the frequency > is 1800:
sr＝103-46.3-33.9*log(10)(f)+13.82*log(10)(h)+((1.11*log(10)(f)-0.7)*1.5-(1.56*log(10)(f)-0.8))-3。
if the base station type is suburban:
sr＝sr+2*(log(10)(f/28))*log(10)(f/28)+5.4+12.28。
when the base station type is open:
sr＝sr+4.78*(log(10)(f))*log(10)(f)-18.33*log(10)(f)+40.94-22.52。
when the frequency is < 1800:
sr＝103-69.55-26.16*log(10)(f)+13.82*log(10)(h+((1.11*log(10)(f)-0.7)*1.5-(1.56*log(10)(f)-0.8))。
if the base station type is suburban:
sr＝sr+2*(log(10)(f/28))*log(10)(f/28)+5.4。
when the base station type is open:
sr＝sr+4.78*(log(10)(f))*log(10)(f)-18.33*log(10)(f)+40.94。
second, calculate the first point on the sector arc
And calculating longitude lat2 and latitude lon2 of a first point on a sector circular arc according to sr, the antenna direction angle d, the longitude lat and the latitude lon, wherein the algorithm is as follows:
longitude and radian are:
latRadian＝lat/180*π
cosL1＝cos(latRadian)
sinL1＝sin(latRadian)
latitude to radian:
lonRadian＝lon/180*π
direction angle radian:
beginAngelRadian＝d/180*π
radius radian:
radius of the Earth is sr/6371229,6371229
radiusRadCos＝cos(radiusRadian)
radiusRadSin＝sin(radiusRadian)
Calculating the longitude and latitude of the first point:
lat2＝asin(sinL1*radiusRadCos+cosL1*radiusRadSin*cos(beginAngel Radian))
lon2＝lonRadian+atan2(sin(beginAngelRadian)*radiusRadSin*cosL1,radiusRadCos-sinL1*sin(lat2))
thirdly, calculating all points on the circular arc of the sector
Using 0.1 as distance interpolation, calculating all points on the circular arc of the sector according to the antenna angle a, the longitude lat2 of the first point, the latitude lon2 of the first point, the longitude lat, the latitude lon, sr and the antenna direction angle d, and concretely comprising the following steps:
calculating interpolation number interPointNum
If sr <0.00001, interPointNum is 1;
if sr > -0.00001, the specific algorithm is as follows:
radian and rotation degree: change angel ═ 2 × acos ((sr-0.1)/sr)/3.1415926 × 180;
if changeAngel <0.00001, InterPointNum is 0;
if changeAngel > -0.00001,
interPointNum ═ floor (abs (a)/changeAngel) + 1;
calculating all points on the arc
If the interPointNum is less than 3, setting the interPointNum to be 3;
if the interPointNum is larger than 140, setting the interPointNum to be 140;
a unit antenna angle varNum is a/interPointNum;
and (3) sequentially calculating the longitude and latitude of each point by a cycle i ═ 1 and i < ═ interPointNum, wherein a specific algorithm is as follows:
latRadian＝lat/180*π
cosL1＝cos(latRadian)
sinL1＝sin(latRadian)
lonRadian＝lon/180*π
beginAngelRadian＝d+(i*varNum)/180*π
radius of the Earth is sr/6371229,6371229
radiusRadCos＝cos(radiusRadian)
radiusRadSin＝sin(radiusRadian)
lat2＝asin(sinL1*radiusRadCos+cosL1*radiusRadSin*cos(beginAngel Radian))
lon2＝lonRadian+atan2(sin(beginAngelRadian)*radiusRadSin*cosL1,radiusRadCos-sinL1*sin(lat2))
In addition, the indoor base station sector algorithm can be divided into the following steps:
the radius of the indoor base station sector is 200 by default, the direction angle is 0, the angle is 360, and the formula is consistent with that of the outdoor base station.
After all points on the circular arc are calculated, a sector object STA is generated by utilizing a GIS function according to the longitude and latitude of the base station cell and all points on the circular arc.
Step two, reading the base station set ST of the user, sequentially taking out ST 1, ST 2, … … and ST n, comparing every two with each other (marked as ST i and ST j), calculating whether the sector object STA under ST i and ST j has a repeat region, if any two base stations have no repeat region, then considering that the user is displaced in the month.
The core algorithm involved in the step two is as follows:
first, the count mNum is 0, which indicates the number of base stations whose sectors do not overlap;
secondly, reading the base station ST of the user, reading ST [1], ST [2], … … and ST [ n ] in sequence, comparing two by two, and recording as ST [ i ] and ST [ j ]: sequentially extracting sector objects STA1, STA2, … … and STAm of ST [ i ], and setting the sector objects STA1, STA2, … … and STAm as x;
thirdly, sequentially judging whether each sector object y of x and ST [ j ] has intersection, and using a function sd.st _ interconnects (x, y);
thirdly, if all sector objects of ST [ i ] and ST [ j ] have no intersection, mNum + 1;
fourthly, when the base station pairwise comparison is completed, if the mNum is greater than 0, the base station is regarded as displacement.
Based on the 80/20 rule, the user group in the same service scene is analyzed, and if 80% of users do not displace in the current month, the users in the service scene are considered to be used in fixed positions and do not displace.
Analyzing the use flow of the user group in the same service scene in the same month, and taking the flow range of the service scene as [ mu-3 sigma, mu +3 sigma ] based on the normal distribution 3 sigma rule]. n: represents the number Xi of users under the user group: represents the current monthly usage flow μ for each user: (X)1+X2+X3+.. + Xn)/n, which represents the average σ of the current-month traffic used by the user in the same service scenario: representing the standard deviation of the flow used by the user in the same service scene in the current month
Through the three steps of service function definition, displacement judgment and flow range definition, the rule of each service scene can be defined and stored in a warehouse.
According to the method, a preliminary business scene rule is accurately defined by utilizing a two-eight law, a GIS technology and a normal distribution 3 sigma rule, meanwhile, a coverage sector of a base station is calculated by utilizing signal attenuation, and the reverse deduction is carried out by utilizing a triangulation positioning principle, so that a user is considered to have displacement if any two base stations do not have repeated coverage areas. And based on the twenty-eight law, if 80% of users in the same scene do not shift, the users in the service scene are determined not to need to shift.
On the basis of the above embodiment, if the service displacement attribute of the internet of things user satisfies the preset condition, the step of determining that the internet of things user is in the super-service scene state specifically includes: and if the service displacement attribute of the user of the Internet of things indicates that the service application range of the user of the Internet of things exceeds the service range set by the service scene, judging that the user of the Internet of things is in a super-service scene state.
The step of judging that the user of the internet of things is in the super-service scene state further comprises the following steps: and auditing the use scene of the Internet of things user in the super-service scene state to obtain the service use state information of the Internet of things user.
After the step of obtaining the service use state information of the internet of things user, the method further includes: and if the service use state of the user of the Internet of things is normal, adjusting the service scene information of the user of the Internet of things.
Specifically, for any internet of things user ub [ i ]. Taking out corresponding service scene data bs according to ub [ i ]. scenario, wherein the service scene data bs comprises a service scene name scenario, whether a voice isCall can be opened, whether a short message isSms can be opened, whether a flow isGprs can be opened, whether an isDisplace can be displaced, a minimum flow (M) minGprs and a maximum flow (M) maxGprs; and taking out corresponding displacement result judgment data ud from uw according to ub [ i ] and cardNO. And if one of the following conditions is met, determining the scene is a super-service scene:
ub [ i ]. call >0, and bs.iscall ═ 0; represents: the service scene corresponding to the user does not allow the voice to be opened, and the user has the voice call duration.
ub [ i ] sms >0, and bs.issms ═ 0; represents: the service scene corresponding to the user does not allow the opening of the short message, and the number of the short messages in the month of the user is more than 0 (the short messages are used).
ub [ i ] gprs >0, and bs.isgprs ═ 0; represents: the service scene corresponding to the user does not allow the flow to be opened, and the flow of the user in the month is larger than 0M (the flow is used).
ub [ i ] gprs > bs.maxgprs; represents: the current monthly flow usage of the user exceeds the maximum flow of the service scenario.
ub [ i ] gprs < bs.mingprs; represents: the current monthly flow usage of the user is lower than the minimum flow of the service scene.
D. display >0, and bs.isplay ═ 0; represents: the service scene corresponding to the user does not allow displacement use, and the user appears displacement use behavior in the current month.
When the internet users appear in the super-service scene, the system can audit the internet users according to the early warning, service supervisors at all levels can audit the super-service scene users issued by the system one by one on site, and audit results and processing measures are fed back.
The feedback result comprises normal use, stealing use, secondary resale and embezzlement, wherein the stealing use, the secondary resale and the embezzlement are specific reasons for causing the use of the super-business scene; the processing measures comprise service scene change and shutdown.
When the audit result is normal use, the system does not process the user of the internet of things, and the system needs to adjust the service scene information according to the part of the user, so that the service rationality of the service scene rule is improved. And adjusting the service scene information according to the audit result of the super-service scene user, wherein the audit result is a user who is normally used, is a user who is misjudged according to the service scene rule and is not actually used in the super-service scene, so that the service scene rule needs to be adjusted according to the part of users.
By the method, based on the service scene monitoring user, whether the user is a risk user is judged according to whether the using behavior is matched with the service scene, and the method is suitable for service risk monitoring of other service ports. Meanwhile, the business scene adjusting method adjusts the business scene rules defined by the data analysis technology based on the offline feedback result to form a self-adaptive ecological circle, so that the business scene rules are more in line with the business development.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a super-service scene early warning system for prevention and control of the internet of things according to an embodiment of the present invention, where the provided system includes: a service scenario definition module 21, a service scenario calculation module 22 and an early warning module 23.
The service scene definition module 21 is configured to define a service scene of an internet of things user according to historical user data of the internet of things user, and obtain service scene information of the internet of things user.
The service scene calculation module 22 is configured to obtain service usage behavior information of the internet of things user, and calculate a service displacement attribute of the internet of things user according to the service usage behavior information and the service scene information.
The early warning module 23 is configured to determine that the internet of things user is in a super-service scene state if the service displacement attribute of the internet of things user meets a preset condition.
The service scene information at least comprises one or more of user behavior scene information, user position scene information and user flow scene information.
Specifically, in this embodiment, the service scenario of each internet of things user is defined one by one according to the historical data of the internet of things user, and the service scenario information that needs to be defined includes whether a voice/short message/traffic function can be used, whether displacement is allowed, and a reasonable range of traffic usage. In the scene definition process, the service function definition, the displacement determination and the flow range definition can be divided, for example, a certain internet of things user can determine that the service scene information of the user is specifically: the flow and short message functions can be opened, the voice function cannot be opened, displacement can be generated, and the flow range is 1G-5G.
Generally, the same service scenario identifier may correspond to the same service scenario; different service scene identifications correspond to different service scenes. According to the service scene identification selected by the user, the users selecting the same service scene identification can be divided into the user groups under the service scene corresponding to the identification. And further collecting the behavior data of the user group for analysis.
After the service scene definition is carried out on the user of the Internet of things, the service use behavior information of the user of the Internet of things in one month is analyzed, so that the service displacement attribute of the user of the Internet of things in the service use process is judged, specifically, the behavior data of the user group of the Internet of things stock is obtained and expressed by a user, the user comprises two parts of data, one part of the data is user data ub, and the user data ub comprises a card number cardNo, a service scene scenario, a month voice call duration call, a month short message number sms and a month use flow (M) gprs; the second part is user base station data us which comprises a card number, a base station cell code, a base station cell longitude, a base station cell latitude, an antenna angle, an azimuth angle, a frequency and an altitude. On the other hand, whether each user of the internet of things is displaced in the current month is calculated according to the us partial data, and result data uw is obtained through a displacement judgment algorithm and comprises a card number cardNo and whether displacement occurs in the current month, 1 represents displacement, and 0: indicating that no displacement has occurred.
For any of the internet of things users ub [ i ]. Taking out corresponding service scene data bs according to ub [ i ]. scenario, wherein the service scene data bs comprises a service scene name scenario, whether a voice isCall can be opened, whether a short message isSms can be opened, whether a flow isGprs can be opened, whether an isDisplace can be displaced, a minimum flow (M) minGprs and a maximum flow (M) maxGprs; and taking out corresponding displacement result judgment data ud from uw according to ub [ i ] and cardNO. For any user of the internet of things, when the service displacement attribute meets the preset condition, the user is judged to be in the state of the super-service scene.
Through the system, a super-service scene recognition model is established based on service scene information, early warning is carried out on the user who recognizes the super-service scene, deep analysis is carried out on the user behavior by fully utilizing data characteristic analysis and calculation, and then a service scene rule is defined according to an analysis result.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the bus 304. Processor 301 may call logic instructions in memory 303 to perform methods including, for example: defining a service scene of an Internet of things user according to historical user data of the Internet of things user to obtain service scene information of the Internet of things user; obtaining service use behavior information of the user of the Internet of things, and calculating a service displacement attribute of the user of the Internet of things according to the service use behavior information and the service scene information; and if the service displacement attribute of the user of the Internet of things meets a preset condition, judging that the user of the Internet of things is in a super-service scene state.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method provided by the above method embodiments, for example, the method includes: defining a service scene of an Internet of things user according to historical user data of the Internet of things user to obtain service scene information of the Internet of things user; obtaining service use behavior information of the user of the Internet of things, and calculating a service displacement attribute of the user of the Internet of things according to the service use behavior information and the service scene information; and if the service displacement attribute of the user of the Internet of things meets a preset condition, judging that the user of the Internet of things is in a super-service scene state.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above method embodiments, for example, including: defining a service scene of an Internet of things user according to historical user data of the Internet of things user to obtain service scene information of the Internet of things user; obtaining service use behavior information of the user of the Internet of things, and calculating a service displacement attribute of the user of the Internet of things according to the service use behavior information and the service scene information; and if the service displacement attribute of the user of the Internet of things meets a preset condition, judging that the user of the Internet of things is in a super-service scene state.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: 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 will 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; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A super-service scene early warning method for Internet of things prevention and control is characterized by comprising the following steps:
defining a service scene of an Internet of things user according to historical user data of the Internet of things user to obtain service scene information of the Internet of things user;
obtaining service use behavior information of the user of the Internet of things, and calculating a service displacement attribute of the user of the Internet of things according to the service use behavior information and the service scene information;
if the service displacement attribute of the user of the Internet of things meets a preset condition, judging that the user of the Internet of things is in a super-service scene state;
the service scene information at least comprises one or more combinations of user behavior scene information, user position scene information and user flow scene information;
the step of defining the service scene of the user of the internet of things according to the historical user data of the user of the internet of things and obtaining the service scene information of the user of the internet of things specifically comprises the following steps:
acquiring basic data of an internet of things user, wherein the basic data at least comprises: card number, signing service scene name, city, business department, group unit name, group unit BOSS code, and one or more data combinations in industry type;
acquiring behavior data of an Internet of things user, wherein the behavior data at least comprises the monthly mobile service use information data of the Internet of things user and base station coding information;
the basic data and the behavior data are used as historical user data of the Internet of things user, and a service scene of the Internet of things user is defined based on the twenty-eight law;
if the service displacement attribute of the user of the internet of things meets a preset condition, the step of judging that the user of the internet of things is in a super-service scene state specifically comprises the following steps:
and if the service displacement attribute of the user of the Internet of things indicates that the service application range of the user of the Internet of things exceeds the service range set by the service scene, judging that the user of the Internet of things is in a super-service scene state.
2. The method according to claim 1, wherein the step of defining the service scenario of the user of the internet of things based on the twenty-eight law specifically comprises:
and judging that more than 80% of users of the Internet of things do not use the first service within a preset time range according to the historical user data, and judging that the first service does not need to be opened in the service scene.
3. The method according to claim 1, wherein the step of defining the service scenario of the user of the internet of things based on twenty-eight law further comprises:
calculating a coverage sector of a base station by using signal attenuation, and calculating the position movement of the user of the Internet of things by using a triangulation positioning principle;
and judging that more than 80% of users of the Internet of things do not have position movement within a preset time range, and judging that the users of the Internet of things do not have position movement in the service scene.
4. The method of claim 1, wherein the step of determining that the user of the internet of things is in the super-business scenario state further comprises:
and auditing the use scene of the Internet of things user in the super-service scene state to obtain the service use state information of the Internet of things user.
5. The method according to claim 4, wherein the step of obtaining the service usage status information of the internet-of-things user further comprises:
and if the service use state of the user of the Internet of things is normal, adjusting the service scene information of the user of the Internet of things.
6. The utility model provides a super business scene early warning system towards thing networking prevention and control which characterized in that includes:
the service scene definition module is used for defining the service scene of the user of the Internet of things according to the historical user data of the user of the Internet of things to obtain the service scene information of the user of the Internet of things;
the service scene calculation module is used for obtaining service use behavior information of the Internet of things user and calculating the service displacement attribute of the Internet of things user according to the service use behavior information and the service scene information;
the early warning module is used for judging that the user of the Internet of things is in a super-service scene state if the service displacement attribute of the user of the Internet of things meets a preset condition;
the service scene information at least comprises one or more combinations of user behavior scene information, user position scene information and user flow scene information;
wherein the service scenario definition module is further configured to:
acquiring basic data of an internet of things user, wherein the basic data at least comprises: card number, signing service scene name, city, business department, group unit name, group unit BOSS code, and one or more data combinations in industry type;
acquiring behavior data of an Internet of things user, wherein the behavior data at least comprises the monthly mobile service use information data of the Internet of things user and base station coding information;
the basic data and the behavior data are used as historical user data of the Internet of things user, and a service scene of the Internet of things user is defined based on the twenty-eight law;
the early warning module is further configured to:
and if the service displacement attribute of the user of the Internet of things indicates that the service application range of the user of the Internet of things exceeds the service range set by the service scene, judging that the user of the Internet of things is in a super-service scene state.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the internet of things prevention and control oriented super-service scene early warning method according to any one of claims 1 to 5 when executing the program.
8. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the internet of things prevention and control oriented hyper-business scenario early warning method according to any one of claims 1 to 5.
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