CN110995543B - Non-invasive method for monitoring abnormal internet surfing behavior of minors - Google Patents
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Abstract
The invention discloses a non-invasive method for monitoring abnormal internet surfing behavior of minors, and belongs to the technical field of computer internet surfing monitoring. The method utilizes a non-invasive electric load identification technology to monitor the service time and the electricity utilization information of a computer in real time; establishing a mapping model of the computer power utilization information and the computer use scene by using historically monitored power utilization information data of the computer under different use scenes and based on a machine learning method of a nuclear limit learning machine; based on the model, identifying the computer use scene at the current moment by utilizing the computer electricity utilization information at the current moment; comparing the allowable use time of the computer set by parents with the allowable use scene of the computer to generate abnormal internet behavior information; and transmitting the abnormal internet behavior information to a mobile phone APP for daily check and payment of the electric charge of the user by utilizing a wireless communication technology. The invention has the advantages that the privacy right of the minor is ensured while the online information of the minor is remotely monitored in real time; the normal operation of the computer can not be influenced.
Description
Technical Field
The invention relates to the technical field of computer internet monitoring, in particular to a non-invasive method for monitoring abnormal internet surfing behaviors of minors.
Background
In recent years, with the improvement of national economy and the standard of living of people, more and more families have installed desktop computers for bedrooms of minor and minor. The computer network can be reasonably used by young children to look up data, master knowledge and make learning progress. However, the juveniles have poor self-control, so that the juveniles are prone to drowning in the network for too much time, and even play games on the net overnight, thereby seriously hindering the progress and healthy growth of the academic industry. And meanwhile, parents are difficult to effectively supervise the system all the time because of busy work. Therefore, finding out a method capable of remotely monitoring the internet surfing behavior of the minor and the young in different places becomes a preoccupation of a plurality of parents.
Existing internet devices are mainly classified into mobile devices (e.g., mobile phones, tablet computers, notebook computers, etc.) and fixed devices (e.g., desktop computers). Since schools prohibit carrying and using mobile devices such as cell phones in the obligation education stage, parents rarely equip minor children with mobile devices such as cell phones. Even if owned, the parent can confiscate without permission to use the product. Therefore, how to effectively monitor the abnormal internet surfing behavior of the minor and the young using the desktop computer becomes a key research direction for the scholars and the technicians at home and abroad.
The technology for monitoring the abnormal internet surfing behavior of minors in the prior art has the following defects: (1) The privacy of children is infringed by the monitoring content, and serious family contradiction is easy to generate; (2) The installation of monitoring software is easily damaged by children, and the actual operation effect is poor; (3) The access authority is set, and the normal use of the computer by children is interfered; (4) A corresponding mobile phone terminal APP is required to be additionally installed to obtain the surfing information of the children; the defects are mainly caused by the fact that most monitoring means are based on an intrusive idea, and then the defects of being damaged, modified, disliked and the like are generated inevitably.
Disclosure of Invention
The invention aims to: aiming at the existing problems, a non-invasive method for monitoring the abnormal internet surfing behavior of the minors is provided, and the use time and the use scene of a computer are monitored in real time by using a non-invasive load identification technology and a machine learning soft measurement modeling method without invading the personal privacy.
The technical scheme adopted by the invention is as follows:
a non-invasive method for monitoring abnormal internet surfing behavior of minors is characterized in that: the method for monitoring the abnormal internet surfing behavior of the non-invasive minors comprises the following specific steps:
s1: acquiring terminal voltage and total current at a user power supply inlet through a household intelligent ammeter, identifying acquired data through a non-intrusive load identification technology, and acquiring real-time service time and real-time power utilization information of a computer;
s2: the machine learning method based on the kernel limit learning machine is characterized in that a mapping model of power utilization information and computer use scenes in different use scenes of a computer in historical use is established;
s3: identifying a computer use scene at the current moment according to the real-time power utilization information obtained in the step S1 based on the established mapping model;
s4: judging whether the computer use scene at the current moment and the computer use time at the current moment are consistent with the computer allowed use scene and the computer allowed use time set by parents, and generating corresponding abnormal internet behavior information;
s5: and transmitting the generated internet surfing behavior information to a mobile phone App for daily checking and paying of the electric charge of the user through a wireless communication technology.
According to the traditional monitoring method for the internet surfing behavior information of the minors, when the internet surfing behavior information of the minors is monitored, the privacy of the minors is easily violated, monitoring software needs to be installed, and the method is easily damaged by people. The invention adopts a non-invasive minor internet behavior monitoring system and method, when the minor internet behavior information is monitored in real time, the privacy of the minor internet behavior information can be protected, the defect that software installed on a traditional computer is easily damaged by children can be avoided, the normal operation of the computer is ensured, and the minor can be remotely monitored in different places more conveniently and directly through a mobile phone App which daily checks and pays electric charges.
Further, in step S1, the specific operation method for identifying the type of the electrical appliance at the current time includes:
s11: acquiring terminal voltage and total current at a power supply inlet by utilizing compressed sensing sparse sampling;
s12: performing per unit, denoising and smoothing processing on the acquired data;
s13: detecting the processed data to obtain the switching time of the household appliance at the current moment;
s14: extracting load characteristics of the household appliance in time periods before and after switching at the current moment;
s15: establishing a load identification model used by historical operating load characteristic data and the type of the household appliance;
s16: identifying the real-time load characteristics based on the established load identification model to obtain the type of the household appliance at the current moment;
s17: and judging whether the household appliance is a computer or not, and if so, performing the step S2.
Further, the specific method for establishing the mapping model in step S2 includes the following steps:
s21: filtering the historically monitored real-time electricity utilization information of the computer under different use scenes;
s22: carrying out standardization processing on the filtered data;
s23: constructing a kernel function of a neural network of the kernel extreme learning machine, and preferably selecting a Radial Basis Function (RBF) kernel function;
s24: optimizing the parameters of the model normalization coefficient and the kernel function by using a genetic algorithm;
s25: and repeating the steps S21-S24 until an optimal target mapping model is obtained.
Further, the specific method for generating the abnormal internet behavior information in step S4 includes:
the computer starting time T1, the computer closing time T2, the latest allowed internet surfing time T3 and the longest allowed service time T meet the following conditions:
s41, when T1 is larger than T2 and larger than T3, generating abnormal use time of the computer;
s42, when the interval duration of the T1 and the T2 is larger than T, generating abnormal use duration of the computer;
s43, when the computer use scene does not belong to the use scene allowed by the parents, generating an abnormal computer use scene;
and S44, when one of the steps S41, S42 and S43 occurs, abnormal internet behavior information is generated.
Further, the computer usage scenario includes playing a large game, watching a video, and browsing a web page.
Further, the electricity consumption information includes steady-state current, steady-state active power, steady-state reactive power, and steady-state current harmonics.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the non-invasive method for monitoring the abnormal internet surfing behavior of the minor, disclosed by the invention, the internet surfing information of the minor is monitored in real time, and meanwhile, the use time and the use scene of a computer are monitored without relating to specific contents (for example, only games are monitored without relating to specific games played), so that the privacy rights of the minor are guaranteed to the greatest extent, and children are easy to accept and can reduce the generation of family contradictions;
2. the invention adopts a non-invasive method for monitoring the abnormal internet surfing behavior of the minors, and a non-invasive technology is used, so that monitoring software does not need to be directly installed on a computer, and the artificial damage and modification of children are avoided;
3. the non-invasive method for monitoring the abnormal internet surfing behavior of the minors does not need to change the service setting of a computer (such as setting a monitoring server, setting access authority, frequently modifying passwords and the like), so that the normal operation of the computer is not influenced;
4. according to the non-invasive monitoring method for the abnormal internet surfing behavior of the minor, a mobile phone APP is not required to be additionally installed, the abnormal internet surfing behavior information of the minor can be remotely and timely obtained in a long-distance mode through daily electricity charge checking and paying APPs, and the use is simple and convenient;
drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a basic flow chart of a non-intrusive monitoring method for the abnormal Internet surfing behavior of minors
FIG. 2 is a schematic diagram of a hardware implementation structure of a non-invasive method for monitoring the abnormal internet surfing behavior of minors
FIG. 3 is a detailed flowchart of a method for monitoring abnormal Internet access behavior of non-invasive minors
FIG. 4 is a flow chart of a computer-based scenario-based recognition model for extreme learning
FIG. 5 is a diagram of a real-time recognition structure of a computer scene recognition model based on a kernel-based extreme learning machine
FIG. 6 is a flow chart of the identification and selection of the non-intrusive load identification technique in step S1
FIG. 7 is a flow chart of the method for monitoring abnormal Internet surfing behavior of non-invasive minors
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
The device modules used in the present invention are commercially available.
The intelligent electric meter with the non-invasive load identification module in the step S1 can be purchased in the market, and the electric power data subentry measurement can be realized.
Example one
The embodiment discloses a non-invasive method for monitoring the abnormal internet surfing behavior of a minor, as shown in fig. 1 and 3, the non-invasive method for monitoring the abnormal internet surfing behavior of the minor specifically comprises the following steps:
s1: acquiring terminal voltage and total current at a user power supply inlet through an intelligent ammeter, and identifying acquired data through a non-invasive load identification module embedded in the intelligent ammeter to acquire real-time service time and real-time power utilization information of a computer; the real-time electricity utilization information comprises real-time steady-state current, real-time steady-state active power, real-time steady-state reactive power and real-time steady-state current harmonic waves of a computer;
s2: a machine learning method based on a kernel-based extreme learning machine is characterized in that a mapping model of computer electricity utilization information and computer use scenes is established by using historically monitored electricity utilization information data of a computer in different use scenes, wherein the different use scenes of the computer comprise use scenes of playing large-scale games, watching videos, browsing webpages and the like.
S3: identifying the computer use scene at the current moment according to the computer real-time power utilization information obtained in the step S1 based on the established mapping model of the computer power utilization information and the computer use scene;
s4: judging whether the computer use scene at the current moment and the computer use time at the current moment are consistent with the computer allowed use scene and the computer allowed use time set by parents, and generating corresponding abnormal internet behavior information; the abnormal internet behavior information comprises abnormal computer use time and a computer use scene which is not in an allowed range, wherein the abnormal computer use time specifically comprises the time when the computer is started to be more than the latest allowed internet time, the time when the computer is closed to be more than the latest allowed internet time, and the use time of the computer is more than the longest allowed internet time;
s5: and transmitting the generated abnormal internet behavior information to a mobile phone terminal APP for daily check and payment of the electric charge of the user through a wireless communication technology.
The real-time electricity utilization information is real-time steady-state current, real-time steady-state active power, real-time steady-state reactive power and real-time steady-state current harmonic wave.
Example two
The embodiment is based on the first embodiment, and discloses a hardware system of a non-invasive method for monitoring the abnormal internet surfing behavior of the minor, which is used for monitoring the abnormal internet surfing behavior of the minor. As shown in fig. 2, the system comprises an acquisition module, a data processing module, a non-intrusive load identification module, a computer usage scene identification module, and an abnormal internet behavior information generation module; the output end of the acquisition module is connected with the input end of the data processing module; the output end of the data processing module is connected with the input end of the non-invasive load identification module; the output end of the non-invasive load identification module is connected with the input end of the computer use scene identification module; the output end of the computer use scene identification module is connected with the input end of the abnormal internet behavior information generation module; the output end of the abnormal internet behavior information generation module is in information transmission with a mobile phone terminal APP for daily electric charge investigation and payment of a user through a wireless communication technology; the acquisition module is embedded in the household intelligent electric meter and is used for acquiring terminal voltage and total current of a user power supply main power supply, and in order to avoid carrying a load of a high-frequency oscillation signal, higher frequency resolution can be obtained by preferably performing compressed sensing sparse sampling; the data processing module is used for carrying out A \ D conversion, per unit value calculation and filtering processing on the data acquired by the acquisition module; the non-invasive load identification module extracts load characteristics for identification processed by the data processing module, inputs the load characteristics into the load identification module, acquires the type information of the electric appliance at the current moment, and transmits the use time information of the computer and the specific electricity utilization information of the computer at the current moment when the identified electric appliance is the computer; the computer use scene identification module is used for identifying the transmitted computer electricity utilization information at the current moment and acquiring the computer use scene at the current moment; the abnormal internet behavior generating module is used for comparing the acquired computer use scene at the current moment with the acquired computer use time at the current moment with the allowed computer use time and the allowed computer use scene set by parents to acquire abnormal internet behavior information; and transmitting the generated abnormal internet behavior information to a mobile phone terminal APP for daily check and payment of the electricity charge of the user by utilizing a wireless communication technology, and reminding parents.
EXAMPLE III
The embodiment is based on the first embodiment, and discloses a non-invasive method for monitoring the abnormal internet surfing behavior of the juveniles, as shown in fig. 4 or 5, the method is based on a machine learning method of a nuclear limit learning machine, and specifically is to perform learning training by using a machine learning algorithm based on the nuclear limit learning machine to generate a computer use scene identification model by taking computer steady-state current, computer steady-state active power, computer steady-state reactive power and computer steady-state current harmonic acquired during the operation of a historical computer as characteristic input quantities. Filtering the steady-state current, the steady-state active power, the steady-state reactive power and the steady-state current harmonic wave of the computer obtained when the historical computer runs, and performing data standardization; selecting and constructing a Radial Basis Function (RBF) kernel function of a neural network of the kernel extreme learning machine: k (x, y) = exp (-b | | | x-y | | | non-luminous flux2) (ii) a Initializing a model normalization coefficient C and a kernel function parameter b; normalizing initialization using genetic algorithmsAnd optimizing the coefficient C and the kernel function parameter b, and training the model to be optimal. Then inputting the online computer electricity utilization information at the current moment into the trained model to obtain a computer use scene at the current moment; the machine learning algorithm based on the kernel limit learning machine can be realized by mainstream programming languages such as Python language, C language and the like, and is packaged into a hardware module.
Example four
The embodiment discloses a non-invasive method for monitoring abnormal internet surfing behavior of minors based on the first embodiment or the second embodiment, and as shown in fig. 6, the specific operation of identifying the type of an electric appliance at the current moment in the step S1 includes:
s11: acquiring terminal voltage and total current at a power supply inlet by using compressed sensing sparse sampling;
s12: performing per unit, denoising and smoothing on the acquired data;
s13: detecting the processed data to obtain the switching time of the household appliance at the current moment;
s14: extracting load characteristics of the household appliance in time periods before and after switching at the current moment;
s15: establishing a load identification model used by the historical operating load characteristic data and the types of the household appliances;
s16: identifying real-time load characteristics based on the established load identification model to obtain the type of the household appliance at the current moment;
s17: and judging whether the household appliance is a computer or not, and if so, performing the step S2.
EXAMPLE five
The embodiment is based on the second embodiment, and discloses a non-invasive method for monitoring the abnormal internet surfing behavior of the minor, as shown in fig. 7, the method is a flow chart for generating the abnormal internet surfing behavior, and the abnormal internet surfing behavior information is generated according to the comparison between the identified computer use time at the current moment and the computer use scene and the reasonable range set by the parents. Specifically, the method comprises the steps that when the computer is started at a time T1, the computer is shut down at a time T2, the latest Internet access permission time T3 and the longest permitted use time T meet (1), T1 is more than T2 and more than T3, the abnormal use time of the computer is generated; (2) When the interval duration of the T1 and the T2 is larger than T, generating the abnormal use duration of the computer; (3) When the computer use scene does not belong to the allowable use scene, generating an abnormal computer use scene; and (3) generating abnormal internet behavior information when one of the steps (1), (2) and (3) occurs.
In conclusion, by adopting the non-invasive method for monitoring the abnormal internet surfing behavior of the minors, the privacy right of the minors is guaranteed to the greatest extent, and monitoring software does not need to be installed on a computer, so that the minors are prevented from being damaged manually; in the using process, the normal operation of the computer can not be influenced, the internet access behavior information of the minors can be remotely and timely obtained in different places, and the operation is more convenient.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification, and to any novel method or process steps or any novel combination of steps disclosed.
Claims (5)
1. A non-invasive method for monitoring the abnormal internet surfing behavior of minors is characterized in that: the method for monitoring the abnormal internet surfing behavior of the non-invasive minors comprises the following specific steps:
s1: acquiring terminal voltage and total current at a user power supply inlet, identifying acquired data through a non-invasive load identification module, and acquiring real-time use time and real-time power utilization information of a computer;
s2: the method comprises the steps of taking the acquired real-time power utilization information of the computer as characteristic input quantity, and establishing a mapping model of the power utilization information and the computer use scene of the computer in historical use under different use scenes based on a machine learning method of a kernel extreme learning machine;
the specific method for establishing the mapping model in the step S2 comprises the following steps: s21: filtering the historically monitored real-time electricity utilization information of the computer under different use scenes;
s22: carrying out standardization processing on the filtered data;
s23: constructing a kernel function of a neural network of a kernel extreme learning machine, and selecting a Radial Basis Function (RBF) kernel function;
s24: optimizing the parameters of the model normalization coefficient and the kernel function by using a genetic algorithm;
s25: repeating the steps S21-S24 until an optimal target mapping model is obtained;
s3: identifying a computer use scene at the current moment according to the real-time power utilization information obtained in the step S1 based on the established mapping model;
s4: judging whether a computer use scene at the current moment and the computer use time at the current moment are consistent with a computer allowed use scene and computer allowed use time set by parents or not, and generating corresponding abnormal internet behavior information;
s5: and transmitting the generated abnormal internet surfing behavior information to a mobile phone App for daily checking and paying of the electric charge of the user through a wireless communication technology.
2. The method for monitoring the abnormal internet surfing behavior of the non-invasive minors as claimed in claim 1, wherein: in step S1, a specific operation method of the identifying method of the non-intrusive load identifying module includes:
s11: acquiring terminal voltage and total current at a power supply inlet by utilizing compressed sensing sparse sampling;
s12: performing per unit, denoising and smoothing on the acquired data;
s13: detecting the processed data to obtain the switching time of the household appliance at the current moment;
s14: extracting load characteristics of the household appliance in time periods before and after switching at the current moment;
s15: establishing a load identification model used by historical operating load characteristic data and the type of the household appliance;
s16: identifying real-time load characteristics based on the established load identification model to obtain the type of the household appliance at the current moment;
s17: and judging whether the household appliance is a computer or not, and if so, performing the step S2.
3. The method for monitoring the abnormal internet surfing behavior of the non-invasive minors as claimed in claim 1, wherein: in the step S4, the generating of the corresponding abnormal internet behavior information includes:
s41: when T1 is greater than T2 and greater than T3, generating abnormal use time of the computer;
s42: when the interval duration of the T1 and the T2 is greater than T, generating abnormal use duration of the computer;
s43, when the computer use scene does not belong to the use scene allowed by the parents, generating an abnormal computer use scene;
s44, generating abnormal internet behavior information when one of the steps S41, S42 and S43 occurs;
wherein, T1 is the opening time of the computer, T2 is the closing time of the computer, T3 is the latest permitted internet access time, and T is the longest permitted service time.
4. The method for monitoring the abnormal internet surfing behavior of the non-invasive minors according to any one of claims 1, 2 and 3, wherein the method comprises the following steps: the computer using scene comprises playing a large-scale game, watching a video and browsing a webpage.
5. The method for monitoring the abnormal internet surfing behavior of the non-invasive minors according to any one of claims 1, 2 and 3, wherein the method comprises the following steps: the electricity utilization information comprises steady-state current, steady-state active power, steady-state reactive power and steady-state current harmonic waves.
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