CN111767739B - PPTL-based system 3 WeChat group on-line monitoring method and system - Google Patents
PPTL-based system 3 WeChat group on-line monitoring method and system Download PDFInfo
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Abstract
The invention belongs to the technical field of network monitoring and discloses a network monitoring system based on PPTL 3 Firstly, a web crawler is used for acquiring text of a WeChat group, and then PPTL is used 3 Describing specific properties and generating corresponding monitors, filtering and obtaining text related to the properties by using a text classification technology and keywords, further judging the TRUE or false of atomic propositions corresponding to the properties, finally combining the atomic propositions into a state sub-formula, and inputting the state sub-formula into the monitors, wherein if a TRUE node is reached, the property is indicated to be TRUE; if the FALSE node is reached, indicating that the property is not established; if the information reaches other nodes, the information acquired at present cannot be judged whether the property is established or not, and the operation needs to be continued. The invention does not need to model the WeChat system, thereby avoiding the problem of state space explosion of the traditional model detection method, and completing the online monitoring of the WeChat group by combining a plurality of technologies.
Description
Technical Field
The invention belongs to the technical field of network monitoring, and particularly relates to a network monitoring system based on PPTL 3 Is a micro-channel group on-line monitoring method and system.
Background
In recent years, the Internet has developed tremendously, and more people start to get used to contact with friends by using Internet applications, share own dynamics, know the current events and find interesting things. Along with the continuous improvement of living standard, the proportion of people in China to use the Internet is also continuously increasing. Social networks are also called social network services, and currently popular social networks exist abroad as Twitter, facebook, instagram, domestic WeChat, QQ, microblog and the like. The social network which is most concerned in China and has the largest number of active people is WeChat. After the WeChat is pushed out, the WeChat is widely used by users and greatly focused on the outside, but the WeChat can spread information and simultaneously bring some privacy and public opinion problems. If the WeChat presents a major problem in privacy, no minor harm is brought to the vast majority of users. In public opinion transmission, social hot events are easy to rapidly spread on WeChat due to the characteristics of strong user viscosity, strong interactivity and the like of WeChat, so that public opinion problems are caused. The hot topics may have negative content that affects social stability. At the same time, there are also users on WeChat who emit a large number of illicit utterances including, but not limited to, pornography, gambling, rumors, etc. If the WeChat information is not monitored forcefully, the bad information can harm the network environment and affect the physical and mental health of teenagers, and the bad information can cause larger public events after being not processed for a long time. Therefore, it is necessary to monitor the operation of the WeChat system and verify the characteristics of the WeChat system in terms of privacy security and public opinion transmission. There are three traditional methods of verifying the nature of a software system: software testing, theorem proving and model detecting. The difficulty of testing is that the proper test case is selected, and only the problem in the program can be proved by the test case, but the problem cannot be proved. Theorem proving technology requires knowledge related to mathematical reasoning and each calculation step is complicated. The model detection technology needs to model a target system, exhausts the state of the system, has higher complexity, and is easy to encounter the problem of state space explosion.
The run-time verification technology is a lightweight software property verification method, and the run-time verification technology judges whether the behavior of the system violates certain properties by monitoring the behavior of the system when the system runs, and once the behavior of the system violates the properties, a monitor can immediately give a warning. The verification technology in the running process does not need to model the system, only concerns the execution track in the running process of the system, and reduces the complexity in the verification process. The run-time verification technique may be real-time in verifying the nature of the system, by monitoring the continuous operation of the system, determining whether the system meets the nature. Compared with theorem proving, the automation degree of the run-time verification technology is higher, and the verification speed is faster. In contrast to testing, runtime verification does not rely on personnel experience nor does it require selection of appropriate test cases. Runtime validation is a formalized method, which is easy to expand and convenient in terms of descriptive properties. Meanwhile, if a model detection method is adopted, the model and an actual social network system are separated, and the model cannot reflect the actual behavior of the social network, namely, the real-time performance is not achieved.
Through the above analysis, the problems and defects existing in the prior art are as follows: the traditional model detection is adopted to verify and analyze the property of the WeChat group, and the WeChat system has the problems of state space explosion and no real-time property due to complex functions.
The difficulty of solving the problems and the defects is as follows: the traditional method for verifying the property of the WeChat group is model detection, but the model detection needs to model the whole WeChat system, the WeChat is a large social network, the modeling is difficult, and the problem of state space explosion exists.
The meaning of solving the problems and the defects is as follows: the invention provides a method for monitoring a micro-community on line, which utilizes a run-time verification technology to judge whether the specific property of the micro-community is established. The invention does not need to model WeChat, has lower complexity and has certain practical value.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a PPTL-based system 3 Is a micro-channel group on-line monitoring method and system.
The invention is realized in such a way that a PPTL-based system 3 On-line monitoring method of micro-channel group based on PPTL 3 The on-line monitoring method of the WeChat group obtains text data of the WeChat group by using a web crawler and uses PPTL 3 Describing the property and generating corresponding property monitor, and then obtaining atomic propositions corresponding to the property according to the text dataAnd (3) inputting the true and false values, combining the atomic propositions into a state sub-formula, and judging whether the specific property is established or not through the operation of the monitor.
Further, the PPTL-based 3 The on-line monitoring method of the WeChat group comprises the following steps:
step one, dynamically acquiring text data of a WeChat group by using a web crawler, and constructing a monitor corresponding to the property to be verified;
step two, text classification and keyword filtering are used for identifying texts with relevant properties, and atomic propositions corresponding to the properties are assigned according to the obtained text information;
and thirdly, converting the monitor into a Java format, combining atomic propositions into a state sub-formula, inputting the state sub-formula into the monitor, and judging whether the property is established according to the operation result of the monitor.
Further, the first step is to determine the property to be satisfied by the micro-community and to use PPTL 3 The formula describes the property, and a finite state automaton, namely a monitor corresponding to the property, is obtained according to the formula.
Further, the method for acquiring the text data of the WeChat group by using the web crawler in the first step includes: preparing a WeChat account, selecting a group for acquiring data, wherein the selected group is active for completing run-time verification; running codes, scanning two-dimensional codes to simulate login webpage version WeChat, and acquiring login states; starting Wireshark to grasp the packet, analyzing the data packet, and returning the data packet to the server; the code obtains the data on the server and stores the data in a file; the web crawler is a Python program, specifically:
defining Chat class to represent basic Chat object, wherein the class has attribute such as WeChat ID, nickname, etc., and has method for sending message chat.send (), obtaining head portrait chat.get_avatar ();
definition class User, friend, member and MP are Chat subclasses, and represent objects such as users, friends, members, public numbers and the like;
and defining a class Bot (), which is used for representing a Web WeChat client, simulating login WeChat through the initial class, monitoring WeChat and dynamically obtaining text data of WeChat group.
Further, the property monitor is a Java application, specifically:
definition class Edge: for describing edges in the property monitoring module;
definition of the class solvent: the method for completing the flow of the run-time verification comprises the following steps:
definition method conjFormula: a sub-formula for taking an atomic proposition into a path representing a system state;
definition method verify: reading the sub-formula into a monitor, operating the monitor and judging the node after the operation state is transferred;
defining a method matchEdge: judging whether the combination of the current atomic propositions meets the information on the corresponding property monitor side or not;
definition method getProp: judging the true or false of the atomic proposition corresponding to the property;
definition method monitor: completing the flow of on-line monitoring of the micro-channel group to obtain a monitoring result;
the method for monitoring the micro-community by the property monitor specifically comprises the following steps: and assigning a value to each atomic proposition of the specific property, taking the assigned atomic propositions as the input of the property monitor, obtaining the next node of the property monitor according to the atomic propositions, and judging whether the property is established or not through the arrived node.
Further, the method for judging the true or false of the atomic proposition comprises the following steps: preprocessing the micro-community text, classifying the micro-community text by using a text classification method integrating word features, text format features and text semantic features, identifying specific words by using a keyword filtering technology, combining text classification and keyword filtering to identify specific information in the text, and further judging the true or false of the atomic proposition.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of: by using web crawlersAcquiring text data of a micro-community by using PPTL 3 Describing the property and generating a corresponding property monitor, obtaining true and false values of atomic propositions corresponding to the property according to the text data, combining the atomic propositions into a state sub-formula, inputting the state sub-formula into the monitor, and judging whether the specific property is established or not through the operation of the monitor.
Another object of the present invention is to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of: text data of WeChat is acquired by using web crawler and PPTL is used for 3 Describing the property and generating a corresponding property monitor, obtaining true and false values of atomic propositions corresponding to the property according to the text data, combining the atomic propositions into a state sub-formula, inputting the state sub-formula into the monitor, and judging whether the specific property is established or not through the operation of the monitor.
It is another object of the present invention to provide a method of operating the PPTL-based system 3 PPTL-based micro-community online monitoring method 3 On-line monitoring system of micro-signal group based on PPTL 3 The micro-community on-line monitoring system comprises:
the web crawler module is used for dynamically acquiring text data of the WeChat group;
the text recognition module is used for recognizing specific information in the micro-community text;
and the operation judging module is used for generating a Java format monitor corresponding to the property and monitoring whether the operation of the micro-community meets the specific property.
Another object of the present invention is to provide a terminal, which carries the PPTL-based terminal 3 Is a micro-community on-line monitoring system; the terminal comprises: a mobile phone APP end and a computer APP end; the APP includes: weChat.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention obtains the text of the WeChat group by using the web crawler and then uses the PPTL 3 To describe properties and generate corresponding monitors, and then obtain property-related text using text classification techniques and keyword filteringThe method further judges whether the atomic proposition corresponding to the property is TRUE or false, finally inputs the atomic proposition into a state sub-formula into a monitor, and indicates that the property is TRUE if the atomic proposition reaches a TRUE node; if the FALSE node is reached, indicating that the property is not established; if the information reaches other nodes, the information acquired at present cannot be judged whether the property is established or not, and the operation needs to be continued. The effect of the present invention in acquiring data is shown in fig. 5. The invention uses PPTL 3 The effect of describing the properties and generating the monitor is shown in fig. 5. The effect of the present invention to verify whether a specific property is established is shown in fig. 6.
The invention does not need to model the WeChat system, thereby avoiding the problem of state space explosion of the traditional model detection method, and completing the online monitoring of the WeChat group by combining a plurality of technologies. The invention provides a PPTL based system 3 The principle basis of the micro-channel group on-line monitoring method is verification during operation, and the method has the characteristics of real time, low complexity and the like, and is an effective method.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the drawings needed in the embodiments of the present application, and it is obvious that the drawings described below are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic illustration of a PPTL-based system provided in an embodiment of the invention 3 The flow chart of the micro-channel group on-line monitoring method.
FIG. 2 is a schematic diagram of a PPTL-based system according to an embodiment of the invention 3 A structural schematic diagram of the micro-community on-line monitoring system;
in the figure: 1. a web crawler module; 2. a text recognition module; 3. and (5) operating a judging module.
FIG. 3 is a schematic diagram of a PPTL-based system according to an embodiment of the invention 3 The implementation flow chart of the micro-channel group on-line monitoring method.
Fig. 4 is a schematic diagram of a micro-group text classification method for fusing word features, text format features and text semantic features provided by the embodiment of the invention.
FIG. 5 is a PPTL according to a description of specific properties 3 The formula builds a schematic diagram of the corresponding monitor.
FIG. 6 is a schematic diagram of the present invention verifying specific properties.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Aiming at the problems existing in the prior art, the invention provides a PPTL-based system 3 The invention is described in detail below with reference to the accompanying drawings.
As shown in FIG. 1, the invention provides a PPTL-based system 3 The micro-community on-line monitoring method comprises the following steps:
s101: dynamically acquiring text data of a WeChat group by using a web crawler, and constructing a monitor corresponding to the property to be verified, wherein the property can be a policy defined by WeChat authorities or a behavior criterion to be observed when a user propagates information;
s102: screening out texts related to the properties by using a text classification technology and a keyword filtering technology, and further judging true and false values of atomic propositions related to the properties;
s103: converting the monitor into Java format, combining the obtained atomic propositions into a sub-formula representing the system state path, inputting the sub-formula into the monitor, and judging whether the property is established according to the operation result of the monitor.
As shown in FIG. 2, the invention provides a PPTL-based system 3 The micro-community on-line monitoring system comprises:
the web crawler module 1 is used for dynamically acquiring text data of the WeChat group.
And the text recognition module 2 is used for recognizing specific information in the micro-community text.
And the operation judging module 3 is used for generating a Java format monitor corresponding to the property and monitoring whether the operation of the micro-community meets the specific property.
The technical scheme of the invention is further described below with reference to the accompanying drawings.
The invention obtains text data of the WeChat group by using the web crawler and uses the PPTL 3 Describing the property and generating a corresponding property monitor, filtering and judging the text related to the property by using text classification and keywords, so as to obtain the true and false value of the atomic proposition corresponding to the property, then combining the atomic propositions into a state sub-formula, inputting the state sub-formula into the property monitor, and automatically and real-time monitoring by the property monitor to judge whether the micro-community accords with the given property when the message is transmitted.
According to the embodiment of the invention based on PPTL as shown in FIG. 3 3 The micro-channel group on-line monitoring method specifically comprises the following steps:
(1) The process of generating a property monitor: first determining the nature to be monitored and then using PPTL 3 Equation P describes the properties: PPTL (PPTL) 3 The determination of equation P requires defining the proposition according to the nature to be verified, and then combining the propositions into the corresponding PPTL according to the logical relationship 3 A formula. Finally, PPTL is provided with 3 The formula input PPTL property monitor generator is converted into a finite state automaton, namely a property monitor, and the edge and node information of the monitor exists in the text invention piece.
PPTL 3 PPTL (Propositional Projection Temporal Logic), PPTL, representing three-value semantics 3 The additional value inconclusive is added, and the additional value inconclusive is expressed by a formula:
PPTL 3 the syntax definition is the same as PPTL, and the syntax definition of PPTL formula p is as follows:
wherein p represents the originalA child proposition, and p belongs to the set Prop, which represents a countable set of atomic propositions. P, P 1 、P 2 Pm is the PPTL formula. The "O" is a sequence operator next, which indicates the next state, and the "prj" is a projection operator project. True, false, →,v is as defined in classical logic, in particular +.>And->Some commonly used derivation formulas in PPTL are defined as follows:
the PPTL property monitor generator is a Java application program for integrating PPTL 3 The formula is converted into a representation form of a finite state automaton, and the obtained finite state automaton is a property monitor of the formula. The edit box above the PPTL property monitor generator interface is used to input the property to be verified, and then clicking the "generate monitor" button on the right side can automatically generate the corresponding property monitoring module according to the input property. The lower left of the interface is a state transition diagram, and three node types exist in the diagram: the TRUE and FALSE nodes indicate that if the event sequence is transferred to the state according to the running time of the system, the current verification result is TRUE or FALSE, and the node marked with a number indicates that whether the property is met or not can not be accurately judged in the current state, and more information is needed, and the verification result is inconclusive. The information on each transfer edge in the property monitoring module is shown at the bottom right of the interface and is stored in the monitor_info txt file.
(2) The text recognition method for the micro-community text comprises the following steps: recognition of a particular category of text is accomplished using text classification techniques and keyword filtering techniques. When the micro-letter group is verified in the running process, a text classification technology and a keyword filtering technology are combined to identify a text of a specific category, so that the true or false of the atomic proposition of the property can be judged. The text classification main steps include extracting text features, calculating feature weights, feature fusion, training a classifier and the like, and the keyword filtering technology is used for filtering text which is difficult to identify by the text classification technology.
The invention comprehensively analyzes the micro-community text and extracts word characteristics, text format characteristics and text semantic characteristics of the micro-community text. And then, assigning the feature weight, carrying out feature fusion, and then classifying by using an SVM classifier to finish the recognition of the specific text. To enhance the ability to identify text, keyword filtering is used in combination with text classification.
The code of the text recognition module is realized by Java, and specifically comprises the following steps:
defining a method train: training a classifier to obtain a classification model;
defining a method prediction: predicting a classification result;
definition method preTree: text preprocessing, namely removing information irrelevant to experiments, such as stop words, special symbols and the like;
definition method countFeature: calculating the value of the feature vector;
definition method vectorData: outputting the quantized data file;
definition method checksense: calling a method provided by a text recognition module to judge whether specific category information exists in the text;
(3) The property monitor is a Java application program, and is configured to receive the output of the text recognition module, analyze and output the output to determine the true and false values of the atomic propositions, generate a combination of the corresponding atomic propositions, and determine whether the combination of the atomic propositions meets the property monitor obtained in (1), and specifically is:
definition class Edge: for describing edges in the property monitoring module;
definition of the class solvent: the method for completing the flow of the run-time verification mainly comprises the following steps:
definition method conjFormula: a sub-formula for taking an atomic proposition into a path representing a system state;
definition method verify: reading the sub-formula into a monitor, operating the monitor and judging the node after the operation state is transferred;
defining a method matchEdge: judging whether the combination of the current atomic propositions meets the information on the corresponding property monitor side or not;
definition method getProp: judging the true or false of the atomic proposition corresponding to the property;
definition method monitor: and (3) completing the flow of online monitoring of the micro-channel group, and obtaining a monitoring result.
The method for online monitoring of the micro-community by the built property monitor specifically comprises the following steps:
the operation judging module completes the flow of the verification in operation by calling a method establish. Firstly, a method relevant to text classification in a text recognition module is called by the method establish, so that SVM model training is completed, and a result is saved. And then calling a Python format web crawler program by using a method provided by a Runtime class, monitoring the operation of WeChat, acquiring the output of the crawler, and continuously updating the variables in the program according to the output. After the chat data is obtained, a method can be called to obtain the value of the atomic proposition. Determining the value of an atomic proposition requires invoking an isensitive () method to identify if the text contains illegal information. And then, the calling method takes the atomic proposition as a state sub-formula representing the system path, inputs the state sub-formula into the monitor, judges the change of the node, and judges whether the operation of the system meets the given property according to the operation result of the monitor. The method specifically comprises the following steps:
a) If the property monitoring module reaches the TRUE node, the given property is established, the TRUE is returned, namely the property is met, and the monitoring of the open source social network is stopped;
b) If the property monitoring module reaches the FALSE node, the given property is not established, the FALSE is returned, namely the property is violated, and the monitoring of the open source social network is stopped;
c) And for other nodes of the property monitoring module, the fact that whether the given property is established is not judged currently is indicated, if the rest text data is not verified, the procedure is returned to judge the true or false of the atomic proposition, otherwise, the procedure is returned to the inconclusive and the process is ended.
The principle of application of the invention is further described below in connection with specific embodiments.
1. Design of web crawlers: the invention uses Python3+Wireshark+Requests to realize the crawling of chat data of a specific micro-community. Wireshark is a common packet grabbing tool, and can analyze network protocols and grab data packets of WeChat webpage ends through some configurations. Requests is a third party library of Python that provides a method to facilitate access to network resources.
The flow of crawling the micro-community data is as follows:
a) Preparing a WeChat account, selecting a group for acquiring data, wherein the selected group is active for completing run-time verification;
b) Running codes, scanning two-dimensional codes to simulate login webpage version WeChat, and acquiring login states;
c) Starting Wireshark to grasp the packet, analyzing the data packet, and returning the data packet to the server;
e) The code obtains the data on the server and stores the data in the file.
2. Generation of a property monitor: firstly analyzing laws and regulations related to the Internet to obtain the property to be followed by a micro-community, and then using PPTL 3 Description is made. For example, the invention provides the property that the behaviour constraint that the citizen cannot release illegal information in the WeChat group is that: after a specific group member issues the illegal information, the group owner should warn the group member which issues the illegal information, warn the group member to stop issuing the illegal information, remind other group members not to forward the illegal information, and withdraw and delete the illegal information. If the group member who issued the offending information remains my element after the group owner alerts, the group owner may move it out of the group and report the user to the relevant institution or network service provider. To simplify the process, we consider that after a member of the group issues two pieces of illegal information, that member should not remain within the group, the group owner should issue a second piece of illegal information at that memberThe offending group is handled within 30 minutes after the rest and removed from the group. The following atomic propositions may be defined:
p: the specific user sends illegal information in the group at present;
q: the group owner performs the group removal treatment on the specific user within 30 minutes;
using PPTL 3 The formula describes the above properties, and the formula describing the properties is (P ∈p ∈len (2)) prj (skip ∈q);
the meaning of P ≡o P ≡len (2) is that the proposition P holds for both the current and next states, with a particular group member counting as a state each time a violation is sent. When a particular group member sends two pieces of offending information in the group, representing his frequent change, the group owner needs to move it out of the group within 30 minutes. If a particular group member remains in the group after 30 minutes, the descriptive nature is not true, and thus the group owner may be considered to be out of duty in this case, without taking over regulatory responsibility. Prj in the formula is a projection operator for running two state sequences on different time scales. When the specific group member transmits the illegal information for the first time, the specific group member is marked as a state, and when the specific group member transmits the illegal information again along with the time, the specific group member is not marked as a state, and the time corresponding to the next state of the group member is a certain time point within 30 minutes after the user b transmits the second piece of illegal information. The sub-formula skip ∈q represents a state sequence of the group owner, and it is necessary to determine whether the atomic proposition Q is established in the second state. For judging the true and false of the atomic propositions P and Q, the implementation of a programming combined with a text classification technology and a keyword-based information filtering technology is required.
And then inputting the formula into a property monitor generator to obtain a text format of the monitor, wherein the text stores information of monitor edges and nodes.
3. From the analysis of 2, it is known that this property requires a decision as to whether the user's speech contains specific information, and therefore, the code of the text recognition module is written for recognizing text. Firstly, preprocessing a text, extracting word characteristics, text format characteristics and text semantic characteristics of the text, then, assigning a characteristic weight, carrying out characteristic fusion, and then, classifying by using an SVM classifier to finish the recognition of a specific text. To enhance the ability to identify text, keyword filtering is used in combination with text classification.
4. The readGraph code of the method for completing the reading of the text information of the monitor and the data storage in the operation judging module is as follows:
after the monitor information is stored by using the data structure, the whole flow of on-line monitoring of the micro-channel group is completed by a method establish, and the specific codes are as follows:
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5. further, the whole online monitoring program is executed to monitor a certain group, and whether the property of the step 2 is established is judged. If not, the group owner of the group is not timely processed with illegal information issued by the group members, and is out of duty and does not take responsibility of monitoring the group.
The technology provided by the invention is used for carrying out online monitoring on a certain micro-letter group, firstly, a Runtime class is used for calling a web crawler in a Java program to dynamically acquire text data of the micro-letter group, and then a Runtime verification technology is used for judging whether the property is established. The results of the verification of the property example set forth above are shown in fig. 5.
The first five rows of the run-time verification result are the relationships of each node and its conversion in the monitor, the next row is the run-time verification result, stop monitor means that the property is found to be not true here in the user's violet sky, i.e. currentNode is false, and the monitor program stops monitoring after monitoring here because we have found users that do not meet the property. The next two lines are chat messages where the user contains offending information. After the user publishes two words containing illegal information, the group owner does not carry out the treatment of moving out of the group within a specified time, so that the group owner of the group can be considered to be out of duty and the responsibility of the group owner is not exhausted.
In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front," "rear," "head," "tail," and the like are used as an orientation or positional relationship based on that shown in the drawings, merely to facilitate description of the invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (6)
1. PPTL-based system 3 The micro-community on-line monitoring method is characterized in that the method is based on PPTL 3 The on-line monitoring method of the WeChat group obtains text data of the WeChat group by using a web crawler and uses PPTL 3 Describing the property and generating a corresponding property monitor, then obtaining true and false values of atomic propositions corresponding to the property according to the text data, combining the atomic propositions into a state sub-formula, inputting the state sub-formula into the monitor, and judging whether the specific property is established or not through the operation of the monitor;
the PPTL-based 3 The on-line monitoring method of the WeChat group comprises the following steps:
step one, dynamically acquiring text data of a WeChat group by using a web crawler, and constructing a monitor corresponding to the property to be verified;
step two, text classification and keyword filtering are used for identifying texts with relevant properties, and atomic propositions corresponding to the properties are assigned according to the obtained text information;
thirdly, converting the monitor into a Java format, combining atomic propositions into a state sub-formula, inputting the state sub-formula into the monitor, and judging whether the property is established according to the operation result of the monitor;
the first step is to determine the property to be satisfied by the micro-community and to use PPTL 3 Describing properties by a formula, and obtaining a finite state automaton, namely a monitor corresponding to the properties according to the formula;
the method for acquiring the text data of the WeChat group by using the web crawler in the first step comprises the following steps: preparing a WeChat account, selecting a group for acquiring data, wherein the selected group is active for completing run-time verification; running codes, scanning two-dimensional codes to simulate login webpage version WeChat, and acquiring login states; starting Wireshark to grasp the packet, analyzing the data packet, and returning the data packet to the server; the code obtains the data on the server and stores the data in a file; the web crawler is a Python program, specifically:
defining Chat class to represent basic Chat object, wherein the class has attribute such as WeChat ID, nickname, etc., and has method for sending message chat.send (), obtaining head portrait chat.get_avatar ();
definition class User, friend, member and MP are Chat subclasses, and represent objects such as users, friends, members, public numbers and the like;
defining a class Bot (), which is used for representing a Web WeChat client, simulating login WeChat through an initial class, monitoring WeChat and dynamically obtaining text data of WeChat group;
the property monitor is a Java application, specifically:
definition class Edge: for describing edges in the property monitoring module;
definition of the class solvent: the method for completing the flow of the run-time verification comprises the following steps:
definition method conjFormula: a sub-formula for taking an atomic proposition into a path representing a system state;
definition method verify: reading the sub-formula into a monitor, operating the monitor and judging the node after the operation state is transferred;
defining a method matchEdge: judging whether the combination of the current atomic propositions meets the information on the corresponding property monitor side or not;
definition method getProp: judging the true or false of the atomic proposition corresponding to the property;
definition method monitor: completing the flow of on-line monitoring of the micro-channel group to obtain a monitoring result;
the method for monitoring the micro-community by the property monitor specifically comprises the following steps: assigning a value to each atomic proposition of a specific property, taking the assigned atomic propositions as the input of a property monitor, obtaining the next node of the property monitor according to the atomic propositions, and judging whether the property is established or not through the arrived node;
2. the PPTL-based material of claim 1 3 The method for online monitoring of the WeChat group is characterized in that the method for judging the true or false of the atomic proposition comprises the following steps: preprocessing the micro-community text, classifying the micro-community text by using a text classification method integrating word features, text format features and text semantic features, identifying specific words by using a keyword filtering technology, combining text classification and keyword filtering to identify specific information in the text, and further judging the true or false of the atomic proposition.
3. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the PPTL-based method of any one of claims 1 to 2 3 The method for monitoring the micro-cell group on line.
4. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the PPTL-based method of any one of claims 1 to 2 3 The method for monitoring the micro-cell group on line.
5. Operating the PPTL-based device of any one of claims 1 to 2 3 PPTL-based micro-community online monitoring method 3 Is characterized in that the micro-community on-line monitoring system based on PPTL 3 The micro-community on-line monitoring system comprises:
the web crawler module is used for dynamically acquiring text data of the WeChat group;
the text recognition module is used for recognizing specific information in the micro-community text;
and the operation judging module is used for generating a Java format monitor corresponding to the property and monitoring whether the operation of the micro-community meets the specific property.
6. A terminal, wherein the terminal is provided with the PPTL-based terminal according to claim 5 3 Is a micro-community on-line monitoring system; the terminal comprises: a mobile phone APP end and a computer APP end; the APP includes: weChat.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105653935A (en) * | 2016-01-08 | 2016-06-08 | 西安电子科技大学 | Social networking system privacy security runtime verification method based on PPTL3 |
| CN107257314A (en) * | 2017-06-05 | 2017-10-17 | 成都知道创宇信息技术有限公司 | A kind of message statistics analysis method based on wechat group |
| CN107679400A (en) * | 2017-08-31 | 2018-02-09 | 西安电子科技大学 | Verification method and system during social networks safe operation based on source code pitching pile |
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| US10826940B2 (en) * | 2016-03-11 | 2020-11-03 | Netskope, Inc. | Systems and methods of enforcing multi-part policies on data-deficient transactions of cloud computing services |
| US10949454B2 (en) * | 2018-10-22 | 2021-03-16 | International Business Machines Corporation | Unsupervised technique for training an engagement classifier in chat-based group conversation |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105653935A (en) * | 2016-01-08 | 2016-06-08 | 西安电子科技大学 | Social networking system privacy security runtime verification method based on PPTL3 |
| CN107257314A (en) * | 2017-06-05 | 2017-10-17 | 成都知道创宇信息技术有限公司 | A kind of message statistics analysis method based on wechat group |
| CN107679400A (en) * | 2017-08-31 | 2018-02-09 | 西安电子科技大学 | Verification method and system during social networks safe operation based on source code pitching pile |
Non-Patent Citations (1)
| Title |
|---|
| 基于关键词共现和社区发现的微博热点主题识别研究;丁晟春;王楠;吴靓婵媛;;现代情报(03);全文 * |
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