CN116668172A - Big data-based communication method and system - Google Patents
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- H—ELECTRICITY
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- H04L63/00—Network architectures or network communication protocols for network security
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- H—ELECTRICITY
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The application relates to a communication method and a system based on big data, comprising the steps of obtaining a communication monitoring permission instruction that a communication information controlled main body is permitted to perform communication monitoring, generating a communication monitoring range setting interface, and obtaining communication monitoring software and communication monitoring sensitive characteristics; acquiring original communication monitoring data, and performing data filtering on the original communication monitoring data to generate actual communication monitoring data; acquiring estimated risk data, generating risk data sets, calculating estimated risk coefficients of the risk data sets, generating risk event data matched with the risk data sets based on big data when the estimated risk coefficients are larger than or equal to standard risk coefficients, displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body. The application also ensures the property safety of the communication information controlled main body on the basis of realizing effective early warning communication.
Description
Technical Field
The application relates to the technical field of communication monitoring, in particular to a communication method and system based on big data.
Background
Big data refers to information which is huge in data volume and cannot be extracted, managed, processed and tidied through a main stream software tool in a reasonable time, and is more positive for helping business operation decision.
Currently, big data has been increasingly applied to other fields, for example, the invention patent with publication number CN109447180a discloses a method of finding telecommunication fraud based on big data and machine learning. The method comprises the following steps: acquiring an original telephone record list, and screening out fraud telephone events in the original telephone record list; analyzing the characteristics and the scene of a called party in a fraud telephone event to obtain a multidimensional characteristic table, and storing the multidimensional characteristic table as preprocessing data; cleaning the preprocessed data to obtain data to be converted; converting the data to be converted into training samples; generating a classifier model using the training samples; and substituting the telephone record data called as the analysis object into the classifier model for early warning. The invention establishes the classifier model based on analyzing the characteristics and the scene of the called party, and carries out telecom fraud early warning on the telephone of the called party as the analysis object, thereby having the advantages of accuracy and effectiveness.
Although the technical scheme in the patent document can establish a classifier model based on analyzing the characteristics and the scene of the called party, and perform telecommunication fraud early warning on the telephone called party as the analysis object, the method has the advantages of accuracy and effectiveness, but the problem that the risk exists due to untimely early warning communication still exists.
Disclosure of Invention
Based on this, it is necessary to provide a communication method and system based on big data, which can ensure effective early warning communication, aiming at the technical problems.
The technical scheme of the invention is as follows:
a big data based communication method, the method comprising:
step S100: acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface;
step S200: the method comprises the steps of obtaining original communication monitoring data generated by a communication monitoring software operated by a communication information controlled main body in a preset monitoring time period, filtering the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered;
Step S300: filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient;
step S400: and when the estimated risk coefficient is greater than or equal to a standard risk coefficient, generating risk event data matched with the risk data set based on big data, displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
Further, step S100: acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface; the method specifically comprises the following steps:
step S110: acquiring a monitoring setting operation of a communication information controlled main body for allowing communication monitoring, and generating a communication monitoring allowing instruction when the monitoring setting operation is matched with a standard setting operation;
Step S120: generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring historical operation data of the communication information controlled main body;
step S130: generating a habit guiding mode according to the historical operation data, and guiding the communication information controlled main body to operate on the communication monitoring range setting interface according to the habit guiding mode;
step S140: acquiring initial monitoring software set by the communication information controlled main body according to the communication monitoring range setting interface under the guidance of the habit guiding mode;
step S150: acquiring commonly used software of the communication information controlled main body, and generating difference monitoring software according to the commonly used software and the initial monitoring software;
step S160: acquiring risk occurrence probability of the difference monitoring software based on a big data technology, selecting recommended monitoring software from the difference monitoring software according to the risk occurrence probability, and displaying the recommended monitoring software to the communication information controlled main body;
step S170: acquiring a software selection operation of the communication information controlled main body on the recommended monitoring software, and generating communication monitoring software according to the software selection operation and the initial monitoring software;
Step S180: and acquiring the communication monitoring sensitive characteristics set by the communication information controlled main body.
Further, the risk data set includes an original risk data set and a risk feature data set; step S300: filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient; the method specifically comprises the following steps:
step S310: acquiring a pre-stored interference data mark according to the actual communication monitoring data, and filtering the interference data in the actual communication monitoring data according to the interference data mark;
step S320: obtaining estimated risk data after filtering interference data according to the interference data mark, wherein the estimated risk data comprises a plurality of estimated risk feature data;
step S330: responding to the obtained estimated risk characteristic data, dividing the estimated risk characteristic data according to a pre-stored time period, and generating an original risk data set after dividing;
Step S340: performing feature extraction from the estimated risk feature data according to pre-stored determined risk features, and generating a risk feature data set after feature extraction is completed;
step S350: comparing the original risk data set with a standard risk data set and generating an original risk coefficient;
step S360: comparing the risk characteristic data set with a standard risk data set and generating a characteristic risk coefficient;
step S370: and adding the original risk coefficient and the characteristic risk coefficient and generating an estimated risk coefficient.
Further, step S400: when the estimated risk coefficient is greater than or equal to a standard risk coefficient, generating risk event data matched with the risk data set based on big data, displaying the risk event data to the communication information controlled main body, and simultaneously locking a fund payment function of the communication information controlled main body; the method specifically comprises the following steps:
step S410: when the estimated risk coefficient is greater than or equal to a standard risk coefficient, splitting the original risk data set in the risk data set in steps, and generating a refinement feature step, wherein the number of the refinement feature steps is a plurality of;
Step S420: deleting each refinement feature step and generating a first induced risk step and a first result risk step;
step S430: carrying out data splitting on a risk characteristic data set in the risk data set and generating risk characteristic steps, wherein the number of the risk characteristic steps is a plurality of risk characteristic steps;
step S440: deleting each risk characteristic step and generating a second risk induction step and a second result risk step;
step S450: performing data matching according to the first induced risk step, the first result risk step, the second induced risk step and the second result risk step based on big data and generating risk event data matched with the first induced risk step, the first result risk step, the second induced risk step and the second result risk step;
step S460: and displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
Further, a big data based communication system, the system comprising:
the communication monitoring setting module is used for acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface;
The monitoring data filtering module is used for obtaining original communication monitoring data generated by the communication monitoring software operated by the communication information controlled main body in a preset monitoring time period, filtering the data of the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered;
the communication risk prediction module is used for filtering interference data according to the actual communication monitoring data, obtaining predicted risk data after filtering the interference data, generating risk data sets according to the predicted risk data, and calculating predicted risk coefficients of the risk data sets, wherein one risk data set corresponds to one predicted risk coefficient;
and the communication early warning indication module is used for generating risk event data matched with the risk data group based on big data when the estimated risk coefficient is greater than or equal to a standard risk coefficient, displaying the risk event data to the communication information controlled main body, and locking the fund payment function of the communication information controlled main body.
Further, the communication monitoring setting module is further configured to:
acquiring a monitoring setting operation of a communication information controlled main body for allowing communication monitoring, and generating a communication monitoring allowing instruction when the monitoring setting operation is matched with a standard setting operation; generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring historical operation data of the communication information controlled main body; generating a habit guiding mode according to the historical operation data, and guiding the communication information controlled main body to operate on the communication monitoring range setting interface according to the habit guiding mode; acquiring initial monitoring software set by the communication information controlled main body according to the communication monitoring range setting interface under the guidance of the habit guiding mode; acquiring commonly used software of the communication information controlled main body, and generating difference monitoring software according to the commonly used software and the initial monitoring software; acquiring risk occurrence probability of the difference monitoring software based on a big data technology, selecting recommended monitoring software from the difference monitoring software according to the risk occurrence probability, and displaying the recommended monitoring software to the communication information controlled main body; acquiring a software selection operation of the communication information controlled main body on the recommended monitoring software, and generating communication monitoring software according to the software selection operation and the initial monitoring software; and acquiring the communication monitoring sensitive characteristics set by the communication information controlled main body.
Further, the risk data set includes an original risk data set and a risk feature data set; the communication risk prediction module is further used for:
acquiring a pre-stored interference data mark according to the actual communication monitoring data, and filtering the interference data in the actual communication monitoring data according to the interference data mark; obtaining estimated risk data after filtering interference data according to the interference data mark, wherein the estimated risk data comprises a plurality of estimated risk feature data; responding to the obtained estimated risk characteristic data, dividing the estimated risk characteristic data according to a pre-stored time period, and generating an original risk data set after dividing; performing feature extraction from the estimated risk feature data according to pre-stored determined risk features, and generating a risk feature data set after feature extraction is completed; comparing the original risk data set with a standard risk data set and generating an original risk coefficient; comparing the risk characteristic data set with a standard risk data set and generating a characteristic risk coefficient; and adding the original risk coefficient and the characteristic risk coefficient and generating an estimated risk coefficient.
Further, the communication early warning indication module is further used for:
when the estimated risk coefficient is greater than or equal to a standard risk coefficient, splitting the original risk data set in the risk data set in steps, and generating a refinement feature step, wherein the number of the refinement feature steps is a plurality of; deleting each refinement feature step and generating a first induced risk step and a first result risk step; carrying out data splitting on a risk characteristic data set in the risk data set and generating risk characteristic steps, wherein the number of the risk characteristic steps is a plurality of risk characteristic steps; deleting each risk characteristic step and generating a second risk induction step and a second result risk step; performing data matching according to the first induced risk step, the first result risk step, the second induced risk step and the second result risk step based on big data and generating risk event data matched with the first induced risk step, the first result risk step, the second induced risk step and the second result risk step; and displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the big data based communication method described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the big data based communication method described above.
The invention has the following technical effects:
according to the communication method and the system based on the big data, the communication monitoring permission instruction that the communication information controlled main body permits communication monitoring is sequentially acquired, the communication monitoring range setting interface is generated according to the communication monitoring permission instruction, and the communication monitoring software and the communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface are acquired; the method comprises the steps of obtaining original communication monitoring data generated by a communication monitoring software operated by a communication information controlled main body in a preset monitoring time period, filtering the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered; filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient; when the estimated risk coefficient is greater than or equal to a standard risk coefficient, generating risk event data matched with the risk data set based on big data, displaying the risk event data to the communication information controlled main body, and simultaneously locking a fund payment function of the communication information controlled main body; in order to ensure the information security monitoring of the communication information controlled main body, and further monitor the communication information controlled main body by setting the communication monitoring sensitive characteristic, but in order to ensure the privacy of the communication information controlled main body, and further obtain the communication monitoring permission instruction that the communication information controlled main body is permitted to perform communication monitoring by the consent of the communication information controlled main body, generate a communication monitoring range setting interface according to the communication monitoring permission instruction, and obtain the communication monitoring software and the communication monitoring sensitive characteristic set by the communication information controlled main body according to the communication monitoring range setting interface, and further filter privacy data by setting the communication monitoring sensitive characteristic, thereby ensuring the complete privacy, namely the communication monitoring sensitive characteristic is the self-set data of the communication information controlled main body, automatically filter out once the privacy data set by the communication information controlled main body is monitored, and further filter out data by filtering according to the actual communication data, and then obtain the interference data according to the actual communication monitoring data, and when a set of estimated risk coefficient is equal to a set of estimated risk coefficient is generated, and a set of estimated risk coefficient is calculated, and then the estimated risk coefficient is matched with the set of estimated risk coefficient is generated when the estimated risk coefficient is matched with the set of the estimated risk coefficient data, and the estimated risk coefficient is calculated, and the risk event data is displayed to the communication information controlled main body, and the fund payment function of the communication information controlled main body is locked, so that the property safety of the communication information controlled main body is ensured on the basis of realizing effective early warning communication.
Drawings
FIG. 1 is a flow chart of a communication method based on big data in one embodiment;
FIG. 2 is a block diagram of a big data based communication system in one embodiment;
FIG. 3 is an internal block diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application 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 application.
In one embodiment, a terminal is provided for: acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface; the method comprises the steps of obtaining original communication monitoring data generated by a communication monitoring software operated by a communication information controlled main body in a preset monitoring time period, filtering the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered; filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient; and when the estimated risk coefficient is greater than or equal to a standard risk coefficient, generating risk event data matched with the risk data set based on big data, displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
The terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
In one embodiment, as shown in fig. 1, there is provided a big data based communication method, the method comprising:
step S100: acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface;
step S200: the method comprises the steps of obtaining original communication monitoring data generated by a communication monitoring software operated by a communication information controlled main body in a preset monitoring time period, filtering the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered;
step S300: filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient;
Step S400: and when the estimated risk coefficient is greater than or equal to a standard risk coefficient, generating risk event data matched with the risk data set based on big data, displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
In this embodiment, in order to ensure information security monitoring of the communication information controlled main body, further monitor the communication information controlled main body by setting the communication monitoring sensitive characteristic, but in order to ensure privacy of the communication information controlled main body, further, by consent of the communication information controlled main body, specifically, obtain a communication monitoring permission instruction that the communication information controlled main body is permitted to perform communication monitoring, generate a communication monitoring range setting interface according to the communication monitoring permission instruction, and obtain communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface, and meanwhile, by setting the communication monitoring sensitive characteristic, further filter privacy data when obtaining original communication monitoring data generated by the communication monitoring software, thereby ensuring complete privacy, namely, the communication monitoring sensitive characteristic is data set by the communication information controlled main body, automatically filter out when privacy data set by the communication information controlled main body is monitored, then, further, by filtering out data, specifically, according to the actual communication monitoring data, and obtaining a set of estimated risk coefficient, when a set of estimated risk coefficient is equal to a set of estimated risk coefficient is obtained, and when a set of estimated risk coefficient is matched with the estimated risk coefficient is calculated, and then, the set of estimated risk coefficient is filtered out corresponding to the estimated risk coefficient is calculated, and the risk event data is displayed to the communication information controlled main body, and the fund payment function of the communication information controlled main body is locked, so that the property safety of the communication information controlled main body is ensured on the basis of realizing effective early warning communication.
In one embodiment, step S100: acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface; the method specifically comprises the following steps:
step S110: acquiring a monitoring setting operation of a communication information controlled main body for allowing communication monitoring, and generating a communication monitoring allowing instruction when the monitoring setting operation is matched with a standard setting operation;
step S120: generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring historical operation data of the communication information controlled main body;
step S130: generating a habit guiding mode according to the historical operation data, and guiding the communication information controlled main body to operate on the communication monitoring range setting interface according to the habit guiding mode;
step S140: acquiring initial monitoring software set by the communication information controlled main body according to the communication monitoring range setting interface under the guidance of the habit guiding mode;
Step S150: acquiring commonly used software of the communication information controlled main body, and generating difference monitoring software according to the commonly used software and the initial monitoring software;
step S160: acquiring risk occurrence probability of the difference monitoring software based on a big data technology, selecting recommended monitoring software from the difference monitoring software according to the risk occurrence probability, and displaying the recommended monitoring software to the communication information controlled main body;
step S170: acquiring a software selection operation of the communication information controlled main body on the recommended monitoring software, and generating communication monitoring software according to the software selection operation and the initial monitoring software;
step S180: and acquiring the communication monitoring sensitive characteristics set by the communication information controlled main body.
In this embodiment, in order to meet the requirement of privacy allowed by the communication information controlled body, the permission allowed by the communication information controlled body needs to be obtained, and the communication information controlled body cannot be monitored without limitation, so that monitoring software needs to be set, specifically, a monitoring setting operation allowing communication monitoring to be performed by the communication information controlled body is firstly obtained, and when the monitoring setting operation is matched with a standard setting operation, a communication monitoring permission instruction is generated; generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring historical operation data of the communication information controlled main body; generating a habit guiding mode according to the historical operation data, and guiding the communication information controlled main body to operate on the communication monitoring range setting interface according to the habit guiding mode; in this way, the guiding mode used by the controlled communication information main body is known in the analysis process of the historical operation data, such as voice guiding, image guiding or video guiding, so that the guiding mode used by the controlled communication information main body is automatically selected, the use requirement of a user is met, the controlled communication information main body obtains initial monitoring software set according to the communication monitoring range setting interface under the guiding of the habit guiding mode, and then when the monitoring software is set, the requirement set by the user is met firstly, specifically, the initial monitoring software set by the controlled communication information main body according to the communication monitoring range setting interface under the guiding of the habit guiding mode is obtained; acquiring commonly used software of the communication information controlled main body, and generating difference monitoring software according to the commonly used software and the initial monitoring software; and then acquiring the risk occurrence probability of the difference monitoring software based on a big data technology, selecting recommended monitoring software from the difference monitoring software according to the risk occurrence probability, and displaying the recommended monitoring software to the communication information controlled main body, so that the probability that each of the difference monitoring software in the existing data has risks is searched based on the big data technology, and if the probability that fraud occurs, the risk occurrence probability is specific, and further, the corresponding monitoring of the software to be monitored can be performed, and further, the security can be ensured while the actual free setting of a user is met.
In one embodiment, the risk data set includes an original risk data set and a risk feature data set; step S300: filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient; the method specifically comprises the following steps:
step S310: acquiring a pre-stored interference data mark according to the actual communication monitoring data, and filtering the interference data in the actual communication monitoring data according to the interference data mark;
step S320: obtaining estimated risk data after filtering interference data according to the interference data mark, wherein the estimated risk data comprises a plurality of estimated risk feature data;
step S330: responding to the obtained estimated risk characteristic data, dividing the estimated risk characteristic data according to a pre-stored time period, and generating an original risk data set after dividing;
step S340: performing feature extraction from the estimated risk feature data according to pre-stored determined risk features, and generating a risk feature data set after feature extraction is completed;
Step S350: comparing the original risk data set with a standard risk data set and generating an original risk coefficient;
step S360: comparing the risk characteristic data set with a standard risk data set and generating a characteristic risk coefficient;
step S370: and adding the original risk coefficient and the characteristic risk coefficient and generating an estimated risk coefficient.
In this embodiment, the interference data mark is a mark of useless data, taking chat record as an example, including redundant symbols, repeated questions, erroneous sentences, and the like, so as to ensure accuracy of subsequent data comparison, further obtain a pre-stored interference data mark according to the actual communication monitoring data, and filter the interference data in the actual communication monitoring data according to the interference data mark; obtaining estimated risk data after filtering interference data according to the interference data marks, wherein the estimated risk data comprises a plurality of estimated risk feature data, the estimated risk feature data is data obtained by filtering useless data in the complete actual communication monitoring data, and in order to accurately evaluate and judge the estimated risk feature data, the data processing is carried out according to time and specific risk features. When data processing is carried out according to time, the pre-estimated risk characteristic data are responded, the pre-estimated risk characteristic data are divided according to a pre-stored time period, and an original risk data set is generated after the division is completed, wherein the time period comprises various conditions, such as one day, one week or a plurality of hours. The setting accords with the actual chat habit of the user, and then the data is conveniently extracted. The determined risk features include marks of specific risk data, such as marks of 'transfer', 'sweep code', 'payment', and the like, and the data which can be extracted based on the features is mostly 'private transfer can be directly performed', 'two-dimensional code of me can be directly scanned', 'direct payment, no matter what' is and 'quick payment'; dividing the estimated risk characteristic data according to a pre-stored time period, and generating an original risk data set after the division is completed; then carrying out feature extraction from the estimated risk feature data according to pre-stored determined risk features, generating a risk feature data set after feature extraction is completed, and then comparing the original risk data set with a standard risk data set and generating an original risk coefficient in order to ensure the accuracy of data acquisition through score generation; then comparing the risk characteristic data set with a standard risk data set and generating a characteristic risk coefficient; and finally, adding the original risk coefficient and the characteristic risk coefficient to generate an estimated risk coefficient, so that the early warning analysis before accurate risk assessment is realized through data interference filtering, wind direction characteristic extraction of data and generation of an assessment coefficient, and the accuracy of communication early warning is improved.
In one embodiment, step S400: when the estimated risk coefficient is greater than or equal to a standard risk coefficient, generating risk event data matched with the risk data set based on big data, displaying the risk event data to the communication information controlled main body, and simultaneously locking a fund payment function of the communication information controlled main body; the method specifically comprises the following steps:
step S410: when the estimated risk coefficient is greater than or equal to a standard risk coefficient, splitting the original risk data set in the risk data set in steps, and generating a refinement feature step, wherein the number of the refinement feature steps is a plurality of;
step S420: deleting each refinement feature step and generating a first induced risk step and a first result risk step;
step S430: carrying out data splitting on a risk characteristic data set in the risk data set and generating risk characteristic steps, wherein the number of the risk characteristic steps is a plurality of risk characteristic steps;
step S440: deleting each risk characteristic step and generating a second risk induction step and a second result risk step;
step S450: performing data matching according to the first induced risk step, the first result risk step, the second induced risk step and the second result risk step based on big data and generating risk event data matched with the first induced risk step, the first result risk step, the second induced risk step and the second result risk step;
Step S460: and displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
In this embodiment, in order to ensure effective communication of the communication information controlled main body, prevent the communication information controlled main body from being damaged by risk information and causing property loss, further indicate that risk may occur at this time when the estimated risk coefficient is greater than or equal to a standard risk coefficient, and further need to perform early warning on information communication of the communication information controlled main body, compared with the problem that the effect caused by simple early warning in the prior art is not obvious, in the method, similar cases are queried through big data technology to perform early warning, real occurrence of risk events is used to perform early warning, thereby realizing more convincing and credibility, specifically, firstly, the original risk data set in the risk data set is split, and refined feature steps are generated, and each refined feature step is pruned, and a first induced risk step and a first result risk step are generated; carrying out data splitting on a risk characteristic data set in the risk data set and generating risk characteristic steps, wherein the number of the risk characteristic steps is a plurality of risk characteristic steps; deleting each risk characteristic step and generating a second induced risk step and a second result risk step, wherein the first induced risk step and the first result risk step are both a starting step and an ending step, for example, the first induced risk step is "the bill is paid firstly, i need pay back again, profit is 100%", the first result risk step can be "quick point transfer, no other consideration is needed, transfer is not needed, and-! And similarly, the second induced risk step and the second result risk step are data of different scenes. The method comprises the steps of taking all steps as retrieval sources, inquiring stored and publicable data based on big data technology, further inquiring risk event data matched with the first induced risk step, the first result risk step, the second induced risk step and the second result risk step, then displaying the risk event data to a communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body, thereby further guaranteeing the property safety of the communication information controlled main body on the basis of realizing effective early warning communication.
In one embodiment, as shown in fig. 2, there is provided a big data based communication system, the system comprising:
the communication monitoring setting module is used for acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface;
the monitoring data filtering module is used for obtaining original communication monitoring data generated by the communication monitoring software operated by the communication information controlled main body in a preset monitoring time period, filtering the data of the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered;
the communication risk prediction module is used for filtering interference data according to the actual communication monitoring data, obtaining predicted risk data after filtering the interference data, generating risk data sets according to the predicted risk data, and calculating predicted risk coefficients of the risk data sets, wherein one risk data set corresponds to one predicted risk coefficient;
And the communication early warning indication module is used for generating risk event data matched with the risk data group based on big data when the estimated risk coefficient is greater than or equal to a standard risk coefficient, displaying the risk event data to the communication information controlled main body, and locking the fund payment function of the communication information controlled main body.
In one embodiment, the communication monitoring setting module is further configured to:
acquiring a monitoring setting operation of a communication information controlled main body for allowing communication monitoring, and generating a communication monitoring allowing instruction when the monitoring setting operation is matched with a standard setting operation; generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring historical operation data of the communication information controlled main body; generating a habit guiding mode according to the historical operation data, and guiding the communication information controlled main body to operate on the communication monitoring range setting interface according to the habit guiding mode; acquiring initial monitoring software set by the communication information controlled main body according to the communication monitoring range setting interface under the guidance of the habit guiding mode; acquiring commonly used software of the communication information controlled main body, and generating difference monitoring software according to the commonly used software and the initial monitoring software; acquiring risk occurrence probability of the difference monitoring software based on a big data technology, selecting recommended monitoring software from the difference monitoring software according to the risk occurrence probability, and displaying the recommended monitoring software to the communication information controlled main body; acquiring a software selection operation of the communication information controlled main body on the recommended monitoring software, and generating communication monitoring software according to the software selection operation and the initial monitoring software; and acquiring the communication monitoring sensitive characteristics set by the communication information controlled main body.
In one embodiment, the risk data set includes an original risk data set and a risk feature data set; the communication risk prediction module is further used for:
acquiring a pre-stored interference data mark according to the actual communication monitoring data, and filtering the interference data in the actual communication monitoring data according to the interference data mark; obtaining estimated risk data after filtering interference data according to the interference data mark, wherein the estimated risk data comprises a plurality of estimated risk feature data; responding to the obtained estimated risk characteristic data, dividing the estimated risk characteristic data according to a pre-stored time period, and generating an original risk data set after dividing; performing feature extraction from the estimated risk feature data according to pre-stored determined risk features, and generating a risk feature data set after feature extraction is completed; comparing the original risk data set with a standard risk data set and generating an original risk coefficient; comparing the risk characteristic data set with a standard risk data set and generating a characteristic risk coefficient; and adding the original risk coefficient and the characteristic risk coefficient and generating an estimated risk coefficient.
In one embodiment, the communication early warning indication module is further configured to:
when the estimated risk coefficient is greater than or equal to a standard risk coefficient, splitting the original risk data set in the risk data set in steps, and generating a refinement feature step, wherein the number of the refinement feature steps is a plurality of; deleting each refinement feature step and generating a first induced risk step and a first result risk step; carrying out data splitting on a risk characteristic data set in the risk data set and generating risk characteristic steps, wherein the number of the risk characteristic steps is a plurality of risk characteristic steps; deleting each risk characteristic step and generating a second risk induction step and a second result risk step; performing data matching according to the first induced risk step, the first result risk step, the second induced risk step and the second result risk step based on big data and generating risk event data matched with the first induced risk step, the first result risk step, the second induced risk step and the second result risk step; and displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
In one embodiment, as shown in fig. 3, a computer device includes a memory storing a computer program and a processor implementing the steps of the big data based communication method described above when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the big data based communication method described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of big data based communication, the method comprising:
acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface; the method comprises the steps of obtaining original communication monitoring data generated by a communication monitoring software operated by a communication information controlled main body in a preset monitoring time period, filtering the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered; filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient; and when the estimated risk coefficient is greater than or equal to a standard risk coefficient, generating risk event data matched with the risk data set based on big data, displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
2. The method according to claim 1, wherein a communication monitoring permission instruction for permitting communication monitoring by a communication information controlled body is obtained, a communication monitoring range setting interface is generated according to the communication monitoring permission instruction, and communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled body according to the communication monitoring range setting interface are obtained; the method specifically comprises the following steps:
acquiring a monitoring setting operation of a communication information controlled main body for allowing communication monitoring, and generating a communication monitoring allowing instruction when the monitoring setting operation is matched with a standard setting operation; generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring historical operation data of the communication information controlled main body; generating a habit guiding mode according to the historical operation data, and guiding the communication information controlled main body to operate on the communication monitoring range setting interface according to the habit guiding mode; acquiring initial monitoring software set by the communication information controlled main body according to the communication monitoring range setting interface under the guidance of the habit guiding mode; acquiring commonly used software of the communication information controlled main body, and generating difference monitoring software according to the commonly used software and the initial monitoring software; acquiring risk occurrence probability of the difference monitoring software based on a big data technology, selecting recommended monitoring software from the difference monitoring software according to the risk occurrence probability, and displaying the recommended monitoring software to the communication information controlled main body; acquiring a software selection operation of the communication information controlled main body on the recommended monitoring software, and generating communication monitoring software according to the software selection operation and the initial monitoring software; and acquiring the communication monitoring sensitive characteristics set by the communication information controlled main body.
3. The big data based communication method of claim 1, wherein the risk data set comprises a raw risk data set and a risk feature data set; filtering interference data according to the actual communication monitoring data, obtaining estimated risk data after filtering the interference data, generating risk data sets according to the estimated risk data, and calculating estimated risk coefficients of the risk data sets, wherein one risk data set corresponds to one estimated risk coefficient; the method specifically comprises the following steps:
acquiring a pre-stored interference data mark according to the actual communication monitoring data, and filtering the interference data in the actual communication monitoring data according to the interference data mark; obtaining estimated risk data after filtering interference data according to the interference data mark, wherein the estimated risk data comprises a plurality of estimated risk feature data; responding to the obtained estimated risk characteristic data, dividing the estimated risk characteristic data according to a pre-stored time period, and generating an original risk data set after dividing; performing feature extraction from the estimated risk feature data according to pre-stored determined risk features, and generating a risk feature data set after feature extraction is completed; comparing the original risk data set with a standard risk data set and generating an original risk coefficient; comparing the risk characteristic data set with a standard risk data set and generating a characteristic risk coefficient; and adding the original risk coefficient and the characteristic risk coefficient and generating an estimated risk coefficient.
4. The big data based communication method according to claim 3, wherein when the estimated risk coefficient is equal to or greater than a standard risk coefficient, generating risk event data matched with the risk data group based on big data, and displaying the risk event data to the communication information controlled body, and locking a fund payment function of the communication information controlled body; the method specifically comprises the following steps:
when the estimated risk coefficient is greater than or equal to a standard risk coefficient, splitting the original risk data set in the risk data set in steps, and generating a refinement feature step, wherein the number of the refinement feature steps is a plurality of; deleting each refinement feature step and generating a first induced risk step and a first result risk step; carrying out data splitting on a risk characteristic data set in the risk data set and generating risk characteristic steps, wherein the number of the risk characteristic steps is a plurality of risk characteristic steps; deleting each risk characteristic step and generating a second risk induction step and a second result risk step; performing data matching according to the first induced risk step, the first result risk step, the second induced risk step and the second result risk step based on big data and generating risk event data matched with the first induced risk step, the first result risk step, the second induced risk step and the second result risk step; and displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
5. A big data based communication system, the system comprising:
the communication monitoring setting module is used for acquiring a communication monitoring permission instruction of a communication information controlled main body for permitting communication monitoring, generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring communication monitoring software and communication monitoring sensitive characteristics set by the communication information controlled main body according to the communication monitoring range setting interface;
the monitoring data filtering module is used for obtaining original communication monitoring data generated by the communication monitoring software operated by the communication information controlled main body in a preset monitoring time period, filtering the data of the original communication monitoring data according to the communication monitoring sensitive characteristics, and generating actual communication monitoring data after the data is filtered;
the communication risk prediction module is used for filtering interference data according to the actual communication monitoring data, obtaining predicted risk data after filtering the interference data, generating risk data sets according to the predicted risk data, and calculating predicted risk coefficients of the risk data sets, wherein one risk data set corresponds to one predicted risk coefficient;
And the communication early warning indication module is used for generating risk event data matched with the risk data group based on big data when the estimated risk coefficient is greater than or equal to a standard risk coefficient, displaying the risk event data to the communication information controlled main body, and locking the fund payment function of the communication information controlled main body.
6. The big data based communication system of claim 5, wherein the communication monitor setting module is further configured to:
acquiring a monitoring setting operation of a communication information controlled main body for allowing communication monitoring, and generating a communication monitoring allowing instruction when the monitoring setting operation is matched with a standard setting operation; generating a communication monitoring range setting interface according to the communication monitoring permission instruction, and acquiring historical operation data of the communication information controlled main body; generating a habit guiding mode according to the historical operation data, and guiding the communication information controlled main body to operate on the communication monitoring range setting interface according to the habit guiding mode; acquiring initial monitoring software set by the communication information controlled main body according to the communication monitoring range setting interface under the guidance of the habit guiding mode; acquiring commonly used software of the communication information controlled main body, and generating difference monitoring software according to the commonly used software and the initial monitoring software; acquiring risk occurrence probability of the difference monitoring software based on a big data technology, selecting recommended monitoring software from the difference monitoring software according to the risk occurrence probability, and displaying the recommended monitoring software to the communication information controlled main body; acquiring a software selection operation of the communication information controlled main body on the recommended monitoring software, and generating communication monitoring software according to the software selection operation and the initial monitoring software; and acquiring the communication monitoring sensitive characteristics set by the communication information controlled main body.
7. The big data based communication system of claim 6, wherein the risk data set includes a raw risk data set and a risk feature data set; the communication risk prediction module is further used for:
acquiring a pre-stored interference data mark according to the actual communication monitoring data, and filtering the interference data in the actual communication monitoring data according to the interference data mark; obtaining estimated risk data after filtering interference data according to the interference data mark, wherein the estimated risk data comprises a plurality of estimated risk feature data; responding to the obtained estimated risk characteristic data, dividing the estimated risk characteristic data according to a pre-stored time period, and generating an original risk data set after dividing; performing feature extraction from the estimated risk feature data according to pre-stored determined risk features, and generating a risk feature data set after feature extraction is completed; comparing the original risk data set with a standard risk data set and generating an original risk coefficient; comparing the risk characteristic data set with a standard risk data set and generating a characteristic risk coefficient; and adding the original risk coefficient and the characteristic risk coefficient and generating an estimated risk coefficient.
8. The big data based communication system of claim 7, wherein the communication early warning indication module is further configured to:
when the estimated risk coefficient is greater than or equal to a standard risk coefficient, splitting the original risk data set in the risk data set in steps, and generating a refinement feature step, wherein the number of the refinement feature steps is a plurality of; deleting each refinement feature step and generating a first induced risk step and a first result risk step; carrying out data splitting on a risk characteristic data set in the risk data set and generating risk characteristic steps, wherein the number of the risk characteristic steps is a plurality of risk characteristic steps; deleting each risk characteristic step and generating a second risk induction step and a second result risk step; performing data matching according to the first induced risk step, the first result risk step, the second induced risk step and the second result risk step based on big data and generating risk event data matched with the first induced risk step, the first result risk step, the second induced risk step and the second result risk step; and displaying the risk event data to the communication information controlled main body, and simultaneously locking the fund payment function of the communication information controlled main body.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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