CN111652740A - Method and device for monitoring online behavior, computer equipment and storage medium - Google Patents
Method and device for monitoring online behavior, computer equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of information security, and provides a method and a device for monitoring online behaviors, computer equipment and a storage medium. In one aspect, the method comprises: after a target behavior is triggered, acquiring behavior data generated by behavior nodes of the target behavior, wherein the target behavior comprises operation behaviors of a plurality of behavior entities on corresponding behavior nodes respectively; calculating the matching rate of the behavior data and an information circulation path; and if the matching rate is lower than a preset threshold value, determining that the target behavior is a risk behavior. The invention solves the technical problem that the related technology can not monitor the on-line behaviors comprising the multi-behavior main body on line, realizes the automatic tracking of the on-line behaviors, and can discover illegal operations and risk behaviors on the blocking line in real time. In addition, the invention also relates to a block chain technology, and the behavior data generated by the behavior node of the target behavior is stored in the block chain.
Description
[ technical field ] A method for producing a semiconductor device
The present invention relates to the field of information security, and in particular, to a method and an apparatus for monitoring online behavior, a computer device, and a storage medium.
[ background of the invention ]
In the related art, monitoring can be performed only for a certain specific behavior, such as risk identification, vulnerability discovery, and the like. For a complex online behavior, such as a complex behavior involving multiple behavior entities, multiple devices, and multiple times, there is often no way to do so.
For example, in the medical insurance business management process, the supervision of medical insurance funds for all subjects is to collect offline clues and check on-site audit, perform evidence collection and verification of clues, and the means is relatively traditional, and the identification process consumes much manpower and material resources, and is lack of professional identification knowledge systems and professionals who perform excessive medical identification. Compared with the fund supervision work of medical insurance business, the compliance evaluation of medical behaviors of doctors becomes a big difficulty; in the medical consumption market, due to the fact that the supply and demand parties have obvious information asymmetry and the individual difference and the complexity of the medical process are added, whether the medical action is reasonable or not is judged, the difficulty is high, the judgment standard is very fuzzy, and even if a third party (a medical insurance payer) is introduced to participate, the third party has medical professional talents, the compliance of the medical action cannot be accurately evaluated. Leading to the prior art being unable to monitor and manage the medical actions of physicians on line.
In view of the above problems in the related art, no effective solution has been found at present.
[ summary of the invention ]
In view of this, embodiments of the present invention provide a method and an apparatus for monitoring online behavior, a computer device, and a storage medium.
In one aspect, an embodiment of the present invention provides a method for monitoring online behavior, where the method includes: after a target behavior is triggered, acquiring behavior data generated by behavior nodes of the target behavior, wherein the target behavior comprises operation behaviors of a plurality of behavior entities on corresponding behavior nodes respectively; calculating the matching rate of the behavior data and an information circulation path; and if the matching rate is lower than a preset threshold value, determining that the target behavior is a risk behavior.
Optionally, the behavior data includes a behavior time sequence, data content, and a node identifier of a behavior node that triggers the behavior data, and calculating a matching rate between the behavior data and an information flow path includes: judging whether the data content is matched with the node identification to obtain a first matching result, judging whether the information flowing direction of the behavior data is consistent with a preset direction according to the behavior time sequence to obtain a second matching result, judging whether the behavior node is in a legal node set or not to obtain a third matching result; and calculating the matching rate of the behavior data based on the first matching result, the second matching result and the third matching result in a weighted mode.
Optionally, judging whether the information flowing direction of the behavior data is consistent with a preset direction according to the behavior time sequence includes: judging whether the generation time of the behavior data is within a preset time or not, wherein the behavior time sequence comprises the generation time and the generation sequence of the behavior data; if the generation time of the behavior data is within a preset time, judging whether the generation sequence between the behavior data and the historical data is a preset sequence; if the generation sequence is a preset sequence, determining that the information circulation direction of the behavior data is consistent with a preset direction; and if the generation sequence is not a preset sequence, determining that the information circulation direction of the behavior data is inconsistent with a preset direction.
Optionally, before calculating the matching rate between the behavior data and the information circulation path, the method further includes: and generating the information circulation path based on the block chain.
Optionally, the generating the information circulation path through the block chain includes: sending the information circulation path of the target behavior to a plurality of block chain nodes; receiving a voting result fed back by at least one block link point aiming at the information circulation path; and when the number of the nodes participating in the voting and the voting result meet preset conditions, setting the information circulation path to be in an effective state.
Optionally, the acquiring the behavior data generated by the behavior node of the target behavior includes: monitoring the number of nodes of a target block chain, wherein the target block chain is used for storing a plurality of behavior data of the target behavior; and if the number of the nodes of the target block chain is increased, reading the node information of the newly-added block chain nodes.
Optionally, before calculating the matching rate between the behavior data and the information circulation path, the method further includes: reading control information carried by the behavior data, wherein the control information is generated by a previous behavior node of a current behavior node according to disease symptom information; and configuring an information circulation path based on the control information, or updating an original information circulation path based on the control information.
In another aspect, an embodiment of the present invention provides an online behavior monitoring apparatus, where the apparatus includes: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring behavior data generated by behavior nodes of a target behavior after the target behavior is triggered, and the target behavior comprises operation behaviors of a plurality of behavior entities on corresponding behavior nodes respectively; the calculation module is used for calculating the matching rate of the behavior data and the information circulation path; and the determining module is used for determining that the target behavior is a risk behavior if the matching rate is lower than a preset threshold.
Optionally, the behavior data includes a behavior time sequence, a data content, and a node identifier of a behavior node that triggers the behavior data, and the computing module includes:
the processing unit is used for judging whether the data content is matched with the node identification to obtain a first matching result, judging whether the information flowing direction of the behavior data is consistent with a preset direction according to the behavior time sequence to obtain a second matching result, and judging whether the behavior node is in a legal node set to obtain a third matching result; and the calculating unit is used for calculating the matching rate of the behavior data based on the first matching result, the second matching result and the third matching result in a weighted mode.
Optionally, the processing unit includes: the first judging subunit is used for judging whether the generation time of the behavior data is within a preset time, wherein the behavior time sequence comprises the generation time and the generation sequence of the behavior data; the second judging subunit is configured to judge whether a generation sequence between the behavior data and the historical data is a preset sequence if the generation time of the behavior data is within a preset time; a determining subunit, configured to determine that an information flow direction of the behavior data is consistent with a preset direction if the generation sequence is a preset sequence; and if the generation sequence is not a preset sequence, determining that the information circulation direction of the behavior data is inconsistent with a preset direction.
Optionally, the apparatus further comprises: and the generating module is used for generating the information circulation path based on the block chain before the calculating module calculates the matching rate of the behavior data and the information circulation path.
Optionally, the generating module includes: the sending unit is used for sending the information circulation path of the target behavior to a plurality of block chain nodes; the receiving unit is used for receiving the voting result fed back by at least one block link point aiming at the information circulation path; and the setting unit is used for setting the information circulation path to be in an effective state when the number of the nodes participating in the voting and the voting result meet preset conditions.
Optionally, the collecting module includes: the monitoring unit is used for monitoring the number of nodes of a target block chain, wherein the target block chain is used for storing a plurality of behavior data of the target behavior; and the reading unit is used for reading the node information of the newly added block chain node if the number of the target block chain nodes is increased.
Optionally, the apparatus further comprises: the reading module is used for reading the control information carried by the behavior data before the calculating module calculates the matching rate of the behavior data and the information circulation path, wherein the control information is generated by the previous behavior node of the current behavior node according to the disease symptom information; and the configuration module is used for configuring an information circulation path based on the control information or updating an original information circulation path based on the control information.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the method and the device, after the target behavior is triggered, the behavior data generated by the behavior nodes of the target behavior are collected, then the matching rate of the behavior data and the information circulation path is calculated, if the matching rate is lower than a preset threshold value, the target behavior is determined to be the risk behavior, and the behavior data of each behavior node is tracked and collected, so that the technical problem that the online behavior comprising the multi-behavior main body cannot be monitored on line in the related technology is solved, the online behavior is automatically tracked, and illegal operation and risk behavior on the line can be found and blocked in real time.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a block diagram of a hardware configuration of an online behavior monitoring computer according to an embodiment of the present invention;
FIG. 2 is a flow diagram of a method of monitoring online behavior according to an embodiment of the invention;
FIG. 3 is a flow diagram of online behavior in an embodiment of the invention;
fig. 4 is a block diagram of a monitoring apparatus for on-line behavior according to an embodiment of the present invention.
[ detailed description ] embodiments
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example 1
The method provided by the first embodiment of the present application may be executed in a server, a computer, or a similar computing device. Taking the example of running on a computer, fig. 1 is a hardware block diagram of a monitoring computer for on-line behavior according to an embodiment of the present invention. As shown in fig. 1, computer 10 may include one or more (only one shown in fig. 1) processors 102 (processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those of ordinary skill in the art that the configuration shown in FIG. 1 is illustrative only and is not intended to limit the configuration of the computer described above. For example, computer 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the monitoring method for on-line behavior in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory located remotely from processor 102, which may be connected to computer 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by the communications provider of computer 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, a method for monitoring an online behavior is provided, and fig. 2 is a flowchart of a method for monitoring an online behavior according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, after the target behavior is triggered, behavior data generated by behavior nodes of the target behavior are collected, wherein the target behavior comprises operation behaviors of a plurality of behavior entities on corresponding behavior nodes respectively;
the target behavior of this embodiment includes a plurality of operation behaviors, and each time an action is completed on a behavior node, the relevant behavior data is uploaded, and for example, a user makes an online disease diagnosis and treatment, the method includes: registration, inquiry, case generation, medicine dispensing and the like, and target behaviors are finished from triggering to finishing by a plurality of nodes and a plurality of devices according to a certain flow direction. The behavior node is a device for triggering behavior data, such as a mobile phone and a computer for installing software.
Step S204, calculating the matching rate of the behavior data and the information circulation path;
the information flow path of this embodiment is what information should be generated by each behavior node of the target behavior, and what device generates the information, who is the last behavior node of the current behavior node, who is the next behavior node, and so on. Fig. 3 is a schematic flow diagram of online behavior in the embodiment of the present invention, where an information flow path is: after triggering, generating data a on the device A, then generating data B on the device B, and finally generating data C on the device C, wherein different devices can be identified and identified through device types (such as mobile phones, handsets and computers), login accounts and network addresses;
and step S206, if the matching rate is lower than a preset threshold, determining that the target behavior is a risk behavior. And if the matching rate is higher than or equal to a preset threshold, determining that the target behavior is a safe behavior, and when the target behavior is a risk behavior, performing risk early warning and performing data interception, such as controlling the output and continuous circulation of behavior data, outputting alarm information and the like.
According to the scheme of the embodiment, after the target behavior is triggered, the behavior data generated by the behavior nodes of the target behavior are collected, then the matching rate of the behavior data and the information circulation path is calculated, if the matching rate is lower than a preset threshold value, the target behavior is determined to be the risk behavior, and the technical problem that the online behavior comprising the multi-behavior main body cannot be monitored online in the related technology is solved by tracking and collecting the behavior data of each behavior node, so that the online behavior is automatically tracked, and illegal operations and risk behaviors on the line can be found and blocked in real time.
The execution main body of the embodiment is a client, a mobile phone, a tablet, a computer, a server, and the like, and can also be applied to a data processing system or a business system such as a service background, a service middle desk, and the like.
Optionally, the behavior data generated by the behavior node that collects the target behavior includes: monitoring the number of nodes of a target block chain, wherein the target block chain is used for storing a plurality of behavior data of a target behavior; and if the number of the nodes of the target block chain is increased, reading the node information of the newly-added block chain nodes.
The scheme identifies and tracks the target behaviors through the behavior identifiers of the target behaviors, and the behavior identifiers can be order numbers, express order numbers, case numbers, user names, identity card numbers and the like according to different target behaviors.
In one example. The target behavior is diagnosis and treatment behavior of a certain disease, and when data is collected, the behavior node is an equipment port through which an information circulation path passes, and the equipment port comprises a diagnosis and treatment access port, a hospital port, a doctor port, a pharmacist port and the like. The data acquisition mainly comprises basic data, clinical paths, behavior data, settlement data and the like. In the embodiment, basic data, clinical paths, behavior data, settlement data and the like of each behavior main body can be encrypted and cannot be tampered through a block union technology, and the behavior data of each behavior node corresponds to one block node. The consistency of the data of the country, province and city and overall region is achieved, the public transparency is realized, the uniqueness is realized, and the reality and transparency of the source data are guaranteed. On the basis, when the medical personnel takes a medical action for medical treatment, the signatures and the time stamps of a plurality of medical practitioners are generated from the initial registration, inquiry, generation of case, medicine dispensing and the like, a block chain is generated, data results generated by the medical action are unified, and the public is transparent. The traceability of data is guaranteed, and if the medical behavior violation problem occurs, the traceability can be directly related to related personnel. The audit is not useless due to the private modification and deletion of information.
In this embodiment, the behavior data includes a behavior time sequence, data content, and a node identifier of a behavior node that triggers the behavior data, and calculating a matching rate of the behavior data and the information flow path includes:
s11, judging whether the data content is matched with the node identification to obtain a first matching result, judging whether the information flow direction of the behavior data is consistent with the preset direction according to the behavior time sequence to obtain a second matching result, and judging whether the behavior node is in a legal node set to obtain a third matching result;
in an implementation manner of this embodiment, determining whether an information flow direction of the behavior data is consistent with a preset direction according to the behavior time sequence includes: judging whether the generation time of the behavior data is within a preset time or not, wherein the behavior time sequence comprises the generation time and the generation sequence of the behavior data; if the generation time of the behavior data is within the preset time, judging whether the generation sequence between the behavior data and the historical data is the preset sequence or not; if the generation sequence is a preset sequence, determining that the information circulation direction of the behavior data is consistent with the preset direction; and if the generation sequence is not the preset sequence, determining that the information circulation direction of the behavior data is inconsistent with the preset direction.
And S12, calculating the matching rate of the behavior data based on the first matching result, the second matching result and the third matching result in a weighted mode.
In one example, calculating a matching rate of the current behavior data to the information flow path includes: judging whether the current data and the historical data are matched with the information flowing direction or not, judging whether the current data is matched with the data trigger equipment or not, setting a weight for each matching item, and finally counting the total matching rate. For example, the information flow path is: device 1, data 1 → device 2, data 2 → device 3, data 3, the current data is data 2, first judging whether the history data of the target behavior is data 1, if not, the information flow direction is not matched, then judging whether data 2 is generated by device 2, if not, the data is not matched.
In the example of introducing the timestamp, it is also necessary to determine whether the generation time of the data 2 matches the preset time in the information circulation path, for example, the data 2 can only be generated within a certain time range, the data 2 must be generated within a certain time after the data 1 is generated, and the like.
In this embodiment, the information flow path of the target behavior may be set in various ways, and may be preset or configured in real time.
In one embodiment, before calculating the matching rate of the behavior data and the information circulation path, the method further comprises: and generating an information flow forwarding path based on the block chain. The process comprises the following steps:
s21, sending information circulation path of target behavior to a plurality of block chain nodes;
s22, receiving the voting result fed back by at least one block chain node aiming at the information circulation path;
and S23, setting the information circulation path to be in an effective state when the number of the nodes participating in the voting and the voting result meet the preset conditions.
In the rule setting process, the monitoring rule in the information circulation path is issued by using a block union technology, the issuing is performed for each supervisor to perform agreement management, the information disclosure is transparent, and consensus is achieved and the rule takes effect by using an election mechanism of a block chain when the vote number and the approval number meet the preset conditions.
According to the method, through the application of the block union technology, the medical practitioners and medical personnel of each medical institution and institution can ensure the data consistency through the signatures and the timestamps, the uniform consensus on the same medical behavior inspection result is achieved, the data consistency is ensured, and the data support is provided for the reality and the effectiveness of risk early warning.
In another embodiment, before calculating the matching rate of the behavior data and the information circulation path, the method further comprises: reading control information carried by behavior data, wherein the control information is generated by a previous behavior node of a current behavior node according to disease symptom information; and configuring an information circulation path based on the control information, or updating the original information circulation path based on the control information.
The behavior data carries a flow control message, wherein the flow control message is used for indicating next behavior data or a plurality of behavior data matched with the current behavior data. By introducing the circulation control information, the information circulation path of the target behavior can be controlled and configured in real time, or the configured information circulation path is changed, and the behavior data carrying the circulation control information can only be generated by a specific node, for example, if the user triggers a disease 1 at the first end, the system allocates the information circulation path 1 of the target behavior corresponding to the disease 1, and if the disease 2 is actually detected or the disease is not detected by the doctor, the information circulation path 1 can be changed into the information circulation path 2 or the information circulation path 3 at the doctor's data end.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
In this embodiment, a monitoring device for online behavior is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a block diagram of a monitoring apparatus for on-line behavior according to an embodiment of the present invention, as shown in fig. 4, the apparatus including: an acquisition module 40, a calculation module 42, a determination module 44, wherein,
the acquisition module 40 is configured to acquire behavior data generated by a behavior node of a target behavior after the target behavior is triggered, where the target behavior includes operation behaviors of a plurality of behavior entities on corresponding behavior nodes respectively;
a calculating module 42, configured to calculate a matching rate between the behavior data and an information circulation path;
a determining module 44, configured to determine that the target behavior is a risk behavior if the matching rate is lower than a preset threshold.
Optionally, the behavior data includes a behavior time sequence, a data content, and a node identifier of a behavior node that triggers the behavior data, and the computing module includes:
the processing unit is used for judging whether the data content is matched with the node identification to obtain a first matching result, judging whether the information flowing direction of the behavior data is consistent with a preset direction according to the behavior time sequence to obtain a second matching result, and judging whether the behavior node is in a legal node set to obtain a third matching result; and the calculating unit is used for calculating the matching rate of the behavior data based on the first matching result, the second matching result and the third matching result in a weighted mode.
Optionally, the processing unit includes: the first judging subunit is used for judging whether the generation time of the behavior data is within a preset time, wherein the behavior time sequence comprises the generation time and the generation sequence of the behavior data; the second judging subunit is configured to judge whether a generation sequence between the behavior data and the historical data is a preset sequence if the generation time of the behavior data is within a preset time; a determining subunit, configured to determine that an information flow direction of the behavior data is consistent with a preset direction if the generation sequence is a preset sequence; and if the generation sequence is not a preset sequence, determining that the information circulation direction of the behavior data is inconsistent with a preset direction.
Optionally, the apparatus further comprises: and the generating module is used for generating the information circulation path based on the block chain before the calculating module calculates the matching rate of the behavior data and the information circulation path.
Optionally, the generating module includes: the sending unit is used for sending the information circulation path of the target behavior to a plurality of block chain nodes; the receiving unit is used for receiving the voting result fed back by at least one block link point aiming at the information circulation path; and the setting unit is used for setting the information circulation path to be in an effective state when the number of the nodes participating in the voting and the voting result meet preset conditions.
Optionally, the collecting module includes: the monitoring unit is used for monitoring the number of nodes of a target block chain, wherein the target block chain is used for storing a plurality of behavior data of the target behavior; and the reading unit is used for reading the node information of the newly added block chain node if the number of the target block chain nodes is increased.
Optionally, the apparatus further comprises: the reading module is used for reading the control information carried by the behavior data before the calculating module calculates the matching rate of the behavior data and the information circulation path, wherein the control information is generated by the previous behavior node of the current behavior node according to the disease symptom information; and the configuration module is used for configuring an information circulation path based on the control information or updating an original information circulation path based on the control information.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 3
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring behavior data generated by behavior nodes of the target behavior after the target behavior is triggered, wherein the target behavior comprises operation behaviors of a plurality of behavior entities on the corresponding behavior nodes respectively;
s2, calculating the matching rate of the behavior data and the information circulation path;
and S3, if the matching rate is lower than a preset threshold, determining that the target behavior is a risk behavior.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring behavior data generated by behavior nodes of the target behavior after the target behavior is triggered, wherein the target behavior comprises operation behaviors of a plurality of behavior entities on the corresponding behavior nodes respectively;
s2, calculating the matching rate of the behavior data and the information circulation path;
and S3, if the matching rate is lower than a preset threshold, determining that the target behavior is a risk behavior.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Claims (10)
1. A method for monitoring online behavior, comprising:
after a target behavior is triggered, acquiring behavior data generated by behavior nodes of the target behavior, wherein the target behavior comprises operation behaviors of a plurality of behavior entities on corresponding behavior nodes respectively;
calculating the matching rate of the behavior data and an information circulation path;
and if the matching rate is lower than a preset threshold value, determining that the target behavior is a risk behavior.
2. The method of claim 1, wherein the behavior data comprises behavior timing, data content, and node identification of a behavior node that triggers the behavior data, and wherein calculating a matching rate of the behavior data to an information flow path comprises:
judging whether the data content is matched with the node identification to obtain a first matching result, judging whether the information flowing direction of the behavior data is consistent with a preset direction according to the behavior time sequence to obtain a second matching result, judging whether the behavior node is in a legal node set or not to obtain a third matching result;
and calculating the matching rate of the behavior data based on the first matching result, the second matching result and the third matching result in a weighted mode.
3. The method of claim 2, wherein determining whether the information flow direction of the behavior data is consistent with a preset direction according to the behavior time sequence comprises:
judging whether the generation time of the behavior data is within a preset time or not, wherein the behavior time sequence comprises the generation time and the generation sequence of the behavior data;
if the generation time of the behavior data is within a preset time, judging whether the generation sequence between the behavior data and the historical data is a preset sequence;
if the generation sequence is a preset sequence, determining that the information circulation direction of the behavior data is consistent with a preset direction; and if the generation sequence is not a preset sequence, determining that the information circulation direction of the behavior data is inconsistent with a preset direction.
4. The method of claim 1, wherein prior to calculating the match rate of the behavior data to the information flow path, the method further comprises:
and generating the information circulation path based on the block chain.
5. The method of claim 4, wherein generating the information flow path based on a block chain comprises:
sending the information circulation path of the target behavior to a plurality of block chain nodes;
receiving a voting result fed back by at least one block link point aiming at the information circulation path;
and when the number of the nodes participating in the voting and the voting result meet preset conditions, setting the information circulation path to be in an effective state.
6. The method of claim 1, wherein collecting behavior data generated by a behavior node of the target behavior comprises:
monitoring the number of nodes of a target block chain, wherein the target block chain is used for storing a plurality of behavior data of the target behavior;
and if the number of the nodes of the target block chain is increased, reading the node information of the newly-added block chain nodes.
7. The method of claim 1, wherein prior to calculating the match rate of the behavior data to the information flow path, the method further comprises:
reading control information carried by the behavior data, wherein the control information is generated by a previous behavior node of a current behavior node according to disease symptom information;
and configuring an information circulation path based on the control information, or updating an original information circulation path based on the control information.
8. An apparatus for monitoring online behavior, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring behavior data generated by behavior nodes of a target behavior after the target behavior is triggered, and the target behavior comprises operation behaviors of a plurality of behavior entities on corresponding behavior nodes respectively;
the calculation module is used for calculating the matching rate of the behavior data and the information circulation path;
and the determining module is used for determining that the target behavior is a risk behavior if the matching rate is lower than a preset threshold.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer storage medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113378332A (en) * | 2021-08-16 | 2021-09-10 | 成都数联云算科技有限公司 | Production equipment group analysis method and device, electronic equipment and computer readable storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106570719A (en) * | 2016-08-24 | 2017-04-19 | 阿里巴巴集团控股有限公司 | Data processing method and apparatus |
| CN109493093A (en) * | 2018-09-26 | 2019-03-19 | 百度在线网络技术（北京）有限公司 | User behavior information processing method and device based on block chain |
| CN109522817A (en) * | 2018-10-27 | 2019-03-26 | 平安医疗健康管理股份有限公司 | A kind of be hospitalized behavior auditing method, block chain node device and the system of patient |
| CN109886683A (en) * | 2019-02-25 | 2019-06-14 | 北京神荼科技有限公司 | Monitor the method, apparatus and storage medium of block chain data |
| CN110300167A (en) * | 2019-06-28 | 2019-10-01 | 京东数字科技控股有限公司 | Business information processing method, equipment and readable storage medium storing program for executing based on block chain |
| CN110516458A (en) * | 2019-08-28 | 2019-11-29 | 腾讯科技（深圳）有限公司 | Data processing method, device and computer equipment based on block chain technology |
| US20200044839A1 (en) * | 2018-08-02 | 2020-02-06 | Boe Technology Group Co., Ltd. | User identity authentication method and device, readable storage medium and computer equipment |
| CN111028936A (en) * | 2019-12-14 | 2020-04-17 | 曹庆恒 | Method, system and equipment for medical examination and reasonable compliance analysis of inspection |
-
2020
- 2020-04-27 CN CN202010341568.5A patent/CN111652740B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106570719A (en) * | 2016-08-24 | 2017-04-19 | 阿里巴巴集团控股有限公司 | Data processing method and apparatus |
| US20200044839A1 (en) * | 2018-08-02 | 2020-02-06 | Boe Technology Group Co., Ltd. | User identity authentication method and device, readable storage medium and computer equipment |
| CN109493093A (en) * | 2018-09-26 | 2019-03-19 | 百度在线网络技术（北京）有限公司 | User behavior information processing method and device based on block chain |
| CN109522817A (en) * | 2018-10-27 | 2019-03-26 | 平安医疗健康管理股份有限公司 | A kind of be hospitalized behavior auditing method, block chain node device and the system of patient |
| CN109886683A (en) * | 2019-02-25 | 2019-06-14 | 北京神荼科技有限公司 | Monitor the method, apparatus and storage medium of block chain data |
| CN110300167A (en) * | 2019-06-28 | 2019-10-01 | 京东数字科技控股有限公司 | Business information processing method, equipment and readable storage medium storing program for executing based on block chain |
| CN110516458A (en) * | 2019-08-28 | 2019-11-29 | 腾讯科技（深圳）有限公司 | Data processing method, device and computer equipment based on block chain technology |
| CN111028936A (en) * | 2019-12-14 | 2020-04-17 | 曹庆恒 | Method, system and equipment for medical examination and reasonable compliance analysis of inspection |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113378332A (en) * | 2021-08-16 | 2021-09-10 | 成都数联云算科技有限公司 | Production equipment group analysis method and device, electronic equipment and computer readable storage medium |
| CN113378332B (en) * | 2021-08-16 | 2021-11-02 | 成都数联云算科技有限公司 | Production equipment group analysis method and device, electronic equipment and computer readable storage medium |
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|---|---|
| CN111652740B (en) | 2022-10-04 |
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