CN114024849B - Intelligent AI operation and maintenance perception method and device - Google Patents
Intelligent AI operation and maintenance perception method and device Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
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- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3604—Software analysis for verifying properties of programs
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/22—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
Abstract
The invention relates to the technical field of intelligent operation and maintenance, and particularly discloses an intelligent AI operation and maintenance perception method and a device, wherein the method comprises the steps of receiving an access request of a user, determining a verification problem, and judging whether a sender of the access request is a real person or not according to the verification problem; when the access request is sent by a real person, monitoring operation data of a user in real time, recording operation time of the operation data, and generating an operation table according to the operation data and the operation time; monitoring a display page in real time, acquiring the conversion time of the display page, and marking a corresponding operation instruction in the operation table according to the conversion time to be used as a conversion node; and carrying out time analysis on the operation table containing the conversion nodes, and generating a detection report according to the time analysis result. The method and the device monitor the operation of the user in real time, analyze the software state based on the operation of the user, have certain predictability, can prepare a processing scheme in advance before the user reports, and are convenient to popularize and use.
Description
Technical Field
The invention relates to the technical field of intelligent operation and maintenance, in particular to an intelligent AI operation and maintenance sensing method and device.
Background
The operation and maintenance, referred to herein as internet operation and maintenance, generally belongs to the technical sector, and is also a 4-large sector for technical support of internet products, such as research, development, testing, and system management, and the division is somewhat different between domestic and foreign companies and between large and small companies. The generation of an internet product generally goes through the process of: project establishment, demand analysis, development of research and development departments, testing of testing departments, deployment and release of operation and maintenance departments and long-term operation and maintenance. Operation and maintenance are essentially the operation and maintenance of each stage of the life cycle of a network, a server and a service, and achieve a consistent and acceptable state in cost, stability and efficiency.
The operation and maintenance is not a design process, but an optimization process, and the operation and maintenance scheme is determined by a user, and the traditional operation and maintenance mode is that corresponding optimization is performed after a report request of the user is received, which obviously means that there is a delay, and how to analyze the state of software in advance is achieved, and the fact that 'no rain is on the silk' is the technical problem that the technical scheme of the invention is intended to solve.
Disclosure of Invention
The invention aims to provide an intelligent AI operation and maintenance sensing method and device to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent AI operation and maintenance perception method and a device thereof are provided, the method comprises the following steps:
receiving an access request of a user, determining a verification problem, and judging whether a sender of the access request is a real person or not according to the verification problem;
when the access request is sent by a real person, monitoring operation data of a user in real time, recording operation time of the operation data, and generating an operation table according to the operation data and the operation time;
monitoring a display page in real time, acquiring the conversion time of the display page, and marking a corresponding operation instruction in the operation table according to the conversion time to be used as a conversion node;
and carrying out time analysis on the operation table containing the conversion nodes, and generating a detection report according to the time analysis result.
As a further limitation of the technical scheme of the invention: the step of receiving the access request of the user, determining a verification problem and judging whether the sender of the access request is a real person according to the verification problem comprises the following steps:
receiving an access request, generating access times, and stopping receiving the access request when the access times are greater than a preset first time threshold;
when the access times are smaller than a preset first time threshold value, determining difficulty levels according to the access times;
determining a verification problem according to the difficulty level; the format of the verification question at least comprises a picture and audio;
and displaying the verification question and obtaining a feedback answer, comparing the feedback answer with a preset reference answer, and judging that the sender is a real person when the feedback answer is the same as the preset reference answer.
As a further limitation of the technical scheme of the invention: the step of analyzing the time of the operation table containing the conversion nodes and generating the detection report according to the time analysis result comprises the following steps:
reading a display page corresponding to the conversion node, and reading an identification code corresponding to the display page in a storage database;
converting the operation table into a plurality of sub-tables according to the conversion node, and sequentially inserting the identification codes into the sub-tables to generate sub-table groups;
reading an operation table, calculating the total operation amount of the operation table, comparing the total operation amount with a preset reference range, and determining the number of marks according to the comparison result;
sequentially comparing the sub-tables in the sub-table group with the reference sub-tables, calculating the offset rate, and sequencing the sub-table group according to the offset rate;
and intercepting the sub-tables with the number of marks from the sorted sub-table group, and generating a detection report according to the sub-tables.
As a further limitation of the technical scheme of the invention: when the verification problem is a gesture action based on a touch screen signal, prompting a user to input the gesture action; acquiring an image contour of an input gesture and the staying time of a user at each detection point on a user terminal; comparing the residence time with a preset time threshold, and generating a sampling point when the residence time is greater than the time threshold; and generating a line segment based on the sampling point, judging whether the line segment is superposed with the image contour, and if so, taking the line segment as an input signal.
As a further limitation of the technical scheme of the invention: the method comprises the following steps:
acquiring the use time of each application program, and marking the application program when the use time is within a preset time range;
counting the marked application programs, sequentially calculating preference values of the marked application programs, and performing ascending arrangement on the marked application programs according to the preference values;
sequentially acquiring search records, and determining keywords according to the search records;
and reading risk questions according to the keywords, displaying the risk questions at regular time, receiving feedback answers, and verifying the identity of the user according to the feedback answers.
As a further limitation of the technical scheme of the invention: the step of counting the marked applications, which in turn calculates the preference values of the marked applications, comprises:
counting the marked application programs, sequentially arranging the marked application programs according to the latest service time corresponding to the marked application programs, and generating marks;
calculating the use frequency of the marked application program, and determining the preference value of the marked application program according to the use frequency and the label;
the calculation formula of the preference value is as follows: p = α B × L; wherein, P is a preference value, B is a parameter generated based on a label, L is a use frequency, and α is a correction coefficient.
The technical scheme of the invention also provides an intelligent AI operation and maintenance perception device, which comprises:
the identity authentication module is used for receiving an access request of a user, determining an authentication problem and judging whether a sender of the access request is a real person or not according to the authentication problem;
the operation table generating module is used for monitoring operation data of a user in real time when the access request is sent by a real person, recording operation time of the operation data, and generating an operation table according to the operation data and the operation time;
the node generation module is used for monitoring the display page in real time, acquiring the conversion time of the display page, and marking a corresponding operation instruction in the operation table according to the conversion time to be used as a conversion node;
and the time analysis module is used for carrying out time analysis on the operation table containing the conversion nodes and generating a detection report according to the time analysis result.
As a further limitation of the technical scheme of the invention: the identity verification module comprises:
the request receiving unit is used for receiving the access request, generating the access times, and stopping receiving the access request when the access times are larger than a preset time threshold;
the level determining unit is used for determining the difficulty level according to the access times when the access times are smaller than a preset time threshold;
the problem determining unit is used for determining a verification problem according to the difficulty level; the format of the verification question at least comprises a picture and audio;
and the comparison unit is used for displaying the verification question, acquiring a feedback answer, comparing the feedback answer with a preset reference answer, and judging that the sender is a real person when the feedback answer is the same as the preset reference answer.
As a further limitation of the technical scheme of the invention: the time analysis module includes:
the identification code reading unit is used for reading the display page corresponding to the conversion node and reading the identification code corresponding to the display page in a storage database;
the sub-table group generating unit is used for converting the operation table into a plurality of sub-tables according to the conversion node, and generating a sub-table group after inserting the identification codes into the sub-tables in sequence;
the quantity determining unit is used for reading an operation table, calculating the total operation quantity of the operation table, comparing the total operation quantity with a preset reference range, and determining the quantity of the marks according to a comparison result;
the sorting unit is used for sequentially comparing the sub-tables in the sub-table group with the reference sub-table, calculating the offset rate and sorting the sub-table group according to the offset rate;
and the report generating unit is used for intercepting the sub-tables with the number of marks in the sorted sub-table group and generating a detection report according to the sub-tables.
As a further limitation of the technical scheme of the invention: the device further comprises:
the marking unit is used for acquiring the use time of each application program, and marking the application program when the use time is within a preset time range;
the statistical unit is used for counting the marked application programs, sequentially calculating preference values of the marked application programs and performing ascending arrangement on the marked application programs according to the preference values;
the keyword determining unit is used for sequentially acquiring search records and determining keywords according to the search records;
and the answer analysis unit is used for reading the risk questions according to the keywords, displaying the risk questions at regular time, receiving feedback answers and verifying the identity of the user according to the feedback answers.
Compared with the prior art, the invention has the beneficial effects that: the method and the device monitor the operation of the user in real time, analyze the software state based on the operation of the user, have certain predictability, can prepare a processing scheme in advance before the user reports, and are convenient to popularize and use.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 shows a flow diagram of an intelligent AI operation and maintenance awareness method.
Fig. 2 shows a first sub-flow block diagram of the intelligent AI operation and maintenance awareness method.
Fig. 3 shows a second sub-flow diagram of the intelligent AI operation and maintenance awareness method.
Fig. 4 shows a block diagram of the structure of the intelligent AI operation and maintenance sensing device.
Fig. 5 shows a block diagram of a component structure of the identity verification module in the intelligent AI operation and maintenance sensing device.
Fig. 6 shows a block diagram of a time analysis module in the intelligent AI operation and maintenance sensing device.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Fig. 1 shows a flow chart of an intelligent AI operation and maintenance perception method, and in an embodiment of the present invention, an intelligent AI operation and maintenance perception method includes:
step S100: receiving an access request of a user, determining a verification problem, and judging whether a sender of the access request is a real person or not according to the verification problem;
the purpose of step S100 is very clear, whether the visitor is a real person is verified, and mainly for this purpose, the computer decrypts the account information by enumerating within a certain range, and then uses the computing resource, which is a common verification code corresponding to the prior art, and is mostly used for some web pages to log in.
Step S200: when the access request is sent by a real person, monitoring operation data of a user in real time, recording operation time of the operation data, and generating an operation table according to the operation data and the operation time;
the above-mentioned content is obtained operation data of the user, and the operation of the user may be various, but at the end, the instruction input process is not many, for example, on a personal computer, only a mode of typing a mouse click is provided, and on a smart phone, only some modes of clicking or sliding a touch screen are provided. The above-mentioned operation data refer to these data.
Step S300: monitoring a display page in real time, acquiring the conversion time of the display page, and marking a corresponding operation instruction in the operation table according to the conversion time to be used as a conversion node;
some of the operations are browsing operations, and some of the operations are page changing operations, the browsing operations representing that the user is satisfied with the page, and the changing operations representing that the user does not like the content so much. During the operation and maintenance, the operation of page replacement is obviously more important than the operation of browsing.
Step S400: and carrying out time analysis on the operation table containing the conversion nodes, and generating a detection report according to the time analysis result.
The operation and maintenance process is not a design process, and is an optimization process, for the generated operation table, if the user spends too long time at a certain stage, the operation process of the user is not smooth, and the less smooth places are the samples which are needed to be obtained most in the operation and maintenance process.
Fig. 2 shows a first sub-flow diagram of the intelligent AI operation and maintenance perception method, where the step of receiving an access request from a user, determining a verification question, and determining whether a sender of the access request is a real person according to the verification question includes steps S101 to S104:
step S101: receiving an access request, generating access times, and stopping receiving the access request when the access times are greater than a preset first time threshold;
step S102: when the access times are smaller than a preset first time threshold value, determining difficulty levels according to the access times;
step S103: determining a verification problem according to the difficulty level; the format of the verification question at least comprises a picture and audio;
step S104: and displaying the verification question and obtaining a feedback answer, comparing the feedback answer with a preset reference answer, and judging that the sender is a real person when the feedback answer is the same as the preset reference answer.
The above is a small innovation of the present invention, and in summary, the verification problem is more than one, and it is conceivable that every person has different abilities, faces a difficult verification problem, and is difficult to pass the verification, which is contrary to the original purpose of the verification problem. For example, one of the existing verification problems is to provide a series of cut images, select pictures including certain features, and then perform multiple continuous verifications with a little error in between or network fluctuation, which is thought to cause the user to feel repugnance, and after the multiple verifications fail, the mood of the user is seriously affected. In an extreme case, if a problem occurs in the system, each verification is wrong by default, and under the verification problem with extremely high difficulty, the user can think that the problem is the problem of the user rather than doubting the system, and the user can think that the user really encounters the condition, the patience of the user can be exhausted, and the system problem is very common, and almost everyone encounters the condition that the verification code shows errors no matter how the verification code is input.
Fig. 3 shows a second sub-flowchart of the intelligent AI operation and maintenance sensing method, where the step of performing time analysis on the operation table containing the conversion node and generating a detection report according to the time analysis result includes steps S401 to S405:
step S401: reading a display page corresponding to the conversion node, and reading an identification code corresponding to the display page in a storage database;
it should be noted that the display page is not limited to only one image, and may be regarded as one page like some short videos; it is conceivable that the display process of these display pages is read from an existing storage database, and there must be an index in the reading process, and this index is the identification code in the content.
Step S402: converting the operation table into a plurality of sub-tables according to the conversion node, and sequentially inserting the identification codes into the sub-tables to generate sub-table groups;
the process of converting the operation table into a plurality of sub-tables through the conversion node is a process of continuously repeating new-insertion data.
Step S403: reading an operation table, calculating the total operation amount of the operation table, comparing the total operation amount with a preset reference range, and determining the number of marks according to the comparison result;
the purpose of step S403 is to perform a simple determination, and if the total operation amount is not large, it indicates that there is a low possibility of a problem, and conversely, if the total operation amount is large, it indicates that there is a high possibility of a problem, and it is the number of markers in concrete response to reality, and the more the number of markers is, the more the samples are read, the more accurate the analysis is.
Step S404: sequentially comparing the sub-tables in the sub-table group with the reference sub-tables, calculating the offset rate, and sequencing the sub-table group according to the offset rate;
the sub-table is the operation corresponding to each page, and if the offset rate of the operation table corresponding to the page is high, the operation table is likely to have problems.
Step S405: intercepting the sub-tables with the number of marks from the sorted sub-table group, and generating a detection report according to the sub-tables;
the number of read sub tables is the number of flags, and it is conceivable that the larger the difference between the total operation amount and the standard operation amount, the larger the number of read sub tables.
Further, when the verification problem is a gesture action based on a touch screen signal, prompting a user to input the gesture action; acquiring an image contour of an input gesture and the staying time of a user at each detection point on a user terminal; comparing the residence time with a preset time threshold, and generating a sampling point when the residence time is greater than the time threshold; and generating a line segment based on the sampling point, judging whether the line segment is superposed with the image contour, and if so, taking the line segment as an input signal.
The above is a specific description of the touch screen signal, and is specifically applied to the access phase of the user.
As a preferred embodiment of the technical solution of the present invention, the method further comprises:
acquiring the use time of each application program, and marking the application program when the use time is within a preset time range;
counting the marked application programs, sequentially calculating preference values of the marked application programs, and performing ascending arrangement on the marked application programs according to the preference values;
sequentially acquiring search records, and determining keywords according to the search records;
and reading risk questions according to the keywords, displaying the risk questions at regular time, receiving feedback answers, and verifying the identity of the user according to the feedback answers.
Because the detection process of the technical scheme of the invention completely depends on the operation of the user, if the user of a certain user terminal changes people, the analysis result is likely to be errors, obviously, the errors are invalid samples, and therefore, the identity of the user needs to be identified. The simplest way is a reward question-answering link, and the questions are determined according to the search records of the user, so that the questions can be distinguished whether the questions are operated by the user.
It is worth mentioning that the above scheme cannot be implemented naturally if the user does not provide the right.
Further, the step of counting the marked applications and sequentially calculating the preference values of the marked applications comprises:
counting the marked application programs, sequentially arranging the marked application programs according to the latest service time corresponding to the marked application programs, and generating marks;
calculating the use frequency of the marked application program, and determining the preference value of the marked application program according to the use frequency and the label;
the calculation formula of the preference value is as follows: p = α B × L; wherein, P is a preference value, B is a parameter generated based on a label, L is a use frequency, and α is a correction coefficient.
The above is a specific calculation process, wherein the application programs are firstly arranged in sequence according to the latest usage time, and as a result of the arrangement, the closer the application program row is to the actual time, the larger the corresponding label is; in other words, the larger the number, the higher the user's preference for the application.
In addition, the use frequency is calculated according to the number of access times in a certain time; it is contemplated that the higher the frequency of use, the higher the user's preference for the application.
It should be noted that if the preference values are in reverse order, the preference values should be inversely proportional to the labels and the corresponding formula should be adjusted to: p = α B/L; wherein, P is a preference value, B is a label, L is a use frequency, and α is a correction coefficient.
Example 2
Fig. 4 is a block diagram illustrating a structure of an intelligent AI operation and maintenance sensing device, in an embodiment of the present invention, an intelligent AI operation and maintenance sensing device 10 includes:
the identity authentication module 11 is used for receiving an access request of a user, determining an authentication problem, and judging whether a sender of the access request is a real person according to the authentication problem;
an operation table generating module 12, configured to monitor operation data of a user in real time when the access request is sent by a real person, record operation time of the operation data, and generate an operation table according to the operation data and the operation time;
the node generation module 13 is configured to monitor a display page in real time, acquire a conversion time of the display page, and mark a corresponding operation instruction in the operation table according to the conversion time to serve as a conversion node;
and the time analysis module 14 is used for performing time analysis on the operation table containing the conversion nodes and generating a detection report according to the time analysis result.
Fig. 5 is a block diagram illustrating a component structure of an identity verification module in the intelligent AI operation and maintenance sensing apparatus, where the identity verification module 11 includes:
a request receiving unit 111, configured to receive an access request, generate an access frequency, and stop receiving the access request when the access frequency is greater than a preset frequency threshold;
a level determining unit 112, configured to determine a difficulty level according to the access times when the access times are smaller than a preset time threshold;
a problem determination unit 113 configured to determine a verification problem according to the difficulty level; the format of the verification question at least comprises a picture and audio;
a comparison unit 114, configured to display the verification question and obtain a feedback answer, compare the feedback answer with a preset reference answer, and determine that the sender is a real person when the feedback answer is the same as the preset reference answer.
Fig. 6 is a block diagram illustrating a structure of a time analysis module in the intelligent AI operation and maintenance sensing device, where the time analysis module 14 includes:
an identification code reading unit 141, configured to read a display page corresponding to the conversion node, and read an identification code corresponding to the display page in a storage database;
a sub-table group generating unit 142, configured to convert the operation table into a plurality of sub-tables according to the conversion node, and sequentially insert the identification codes into the sub-tables to generate a sub-table group;
a quantity determining unit 143, configured to read an operation table, calculate a total operation amount of the operation table, compare the total operation amount with a preset reference range, and determine a quantity of the marks according to a comparison result;
a sorting unit 144, configured to sequentially compare sub-tables in the sub-table group with a reference sub-table, calculate an offset rate, and sort the sub-table group according to the offset rate;
and the report generating unit is used for intercepting the sub-tables with the number of marks in the sorted sub-table group and generating a detection report according to the sub-tables.
Further, the apparatus further comprises:
the marking unit is used for acquiring the use time of each application program, and marking the application program when the use time is within a preset time range;
the statistical unit is used for counting the marked application programs, sequentially calculating preference values of the marked application programs and performing ascending arrangement on the marked application programs according to the preference values;
the keyword determining unit is used for sequentially acquiring search records and determining keywords according to the search records;
and the answer analysis unit is used for reading the risk questions according to the keywords, displaying the risk questions at regular time, receiving feedback answers and verifying the identity of the user according to the feedback answers.
The functions that can be realized by the intelligent AI operation and maintenance perception method are all completed by a computer device, and the computer device comprises one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and is loaded and executed by the one or more processors to realize the functions of the intelligent AI operation and maintenance perception method.
The processor fetches instructions and analyzes the instructions one by one from the memory, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. An intelligent AI operation and maintenance perception method, characterized in that the method comprises:
receiving an access request of a user, determining a verification problem, and judging whether a sender of the access request is a real person or not according to the verification problem;
when the access request is sent by a real person, monitoring operation data of a user in real time, recording operation time of the operation data, and generating an operation table according to the operation data and the operation time;
monitoring a display page in real time, acquiring the conversion time of the display page, and marking a corresponding operation instruction in the operation table according to the conversion time to be used as a conversion node;
carrying out time analysis on an operation table containing the conversion nodes, and generating a detection report according to the time analysis result;
the step of analyzing the time of the operation table containing the conversion nodes and generating the detection report according to the time analysis result comprises the following steps:
reading a display page corresponding to the conversion node, and reading an identification code corresponding to the display page in a storage database;
converting the operation table into a plurality of sub-tables according to the conversion node, and sequentially inserting the identification codes into the sub-tables to generate sub-table groups;
reading an operation table, calculating the total operation amount of the operation table, comparing the total operation amount with a preset reference range, and determining the number of marks according to the comparison result;
sequentially comparing the sub-tables in the sub-table group with the reference sub-tables, calculating the offset rate, and sequencing the sub-table group according to the offset rate;
and intercepting the sub-tables with the number of marks from the sorted sub-table group, and generating a detection report according to the sub-tables.
2. The intelligent AI operation and maintenance perception method according to claim 1, wherein the receiving of the access request of the user and the determination of the verification question, the determining whether the sender of the access request is a real person according to the verification question, comprises:
receiving an access request, generating access times, and stopping receiving the access request when the access times are greater than a preset first time threshold;
when the access times are smaller than a preset first time threshold value, determining difficulty levels according to the access times;
determining a verification problem according to the difficulty level; the format of the verification question at least comprises a picture and audio;
and displaying the verification question and obtaining a feedback answer, comparing the feedback answer with a preset reference answer, and judging that the sender is a real person when the feedback answer is the same as the preset reference answer.
3. The intelligent AI operation and maintenance perception method according to claim 1 wherein when the verification problem is a gesture action based on a touch screen signal, prompting the user to input the gesture action; acquiring an image contour of an input gesture and the staying time of a user at each detection point on a user terminal; comparing the residence time with a preset time threshold, and generating a sampling point when the residence time is greater than the time threshold; and generating a line segment based on the sampling point, judging whether the line segment is superposed with the image contour, and if so, taking the line segment as an input signal.
4. The intelligent AI operation and maintenance awareness method according to any one of claims 1-3, wherein the method comprises:
acquiring the use time of each application program, and marking the application program when the use time is within a preset time range;
counting the marked application programs, sequentially calculating preference values of the marked application programs, and performing ascending arrangement on the marked application programs according to the preference values;
sequentially acquiring search records, and determining keywords according to the search records;
and reading risk questions according to the keywords, displaying the risk questions at regular time, receiving feedback answers, and verifying the identity of the user according to the feedback answers.
5. The intelligent AI operation and maintenance awareness method according to claim 4, wherein the step of counting the tagged applications, in turn, calculating the preference values of the tagged applications comprises:
counting the marked application programs, sequentially arranging the marked application programs according to the latest service time corresponding to the marked application programs, and generating marks;
calculating the use frequency of the marked application program, and determining the preference value of the marked application program according to the use frequency and the label;
the calculation formula of the preference value is as follows: p = α B × L; wherein, P is a preference value, B is a parameter generated based on a label, L is a use frequency, and α is a correction coefficient.
6. An intelligent AI operation and maintenance awareness apparatus, comprising:
the identity authentication module is used for receiving an access request of a user, determining an authentication problem and judging whether a sender of the access request is a real person or not according to the authentication problem;
the operation table generating module is used for monitoring operation data of a user in real time when the access request is sent by a real person, recording operation time of the operation data, and generating an operation table according to the operation data and the operation time;
the node generation module is used for monitoring the display page in real time, acquiring the conversion time of the display page, and marking a corresponding operation instruction in the operation table according to the conversion time to be used as a conversion node;
the time analysis module is used for carrying out time analysis on the operation table containing the conversion nodes and generating a detection report according to the time analysis result;
the time analysis module includes:
the identification code reading unit is used for reading the display page corresponding to the conversion node and reading the identification code corresponding to the display page in a storage database;
the sub-table group generating unit is used for converting the operation table into a plurality of sub-tables according to the conversion node, and generating a sub-table group after inserting the identification codes into the sub-tables in sequence;
the quantity determining unit is used for reading an operation table, calculating the total operation quantity of the operation table, comparing the total operation quantity with a preset reference range, and determining the quantity of the marks according to a comparison result;
the sorting unit is used for sequentially comparing the sub-tables in the sub-table group with the reference sub-table, calculating the offset rate and sorting the sub-table group according to the offset rate;
and the report generating unit is used for intercepting the sub-tables with the number of marks in the sorted sub-table group and generating a detection report according to the sub-tables.
7. The intelligent AI operation and maintenance awareness apparatus according to claim 6, wherein the identity verification module comprises:
the request receiving unit is used for receiving the access request, generating the access times, and stopping receiving the access request when the access times are larger than a preset time threshold;
the level determining unit is used for determining the difficulty level according to the access times when the access times are smaller than a preset time threshold;
the problem determining unit is used for determining a verification problem according to the difficulty level; the format of the verification question at least comprises a picture and audio;
and the comparison unit is used for displaying the verification question, acquiring a feedback answer, comparing the feedback answer with a preset reference answer, and judging that the sender is a real person when the feedback answer is the same as the preset reference answer.
8. The intelligent AI operation and maintenance awareness apparatus according to claim 6 or 7, further comprising:
the marking unit is used for acquiring the use time of each application program, and marking the application program when the use time is within a preset time range;
the statistical unit is used for counting the marked application programs, sequentially calculating preference values of the marked application programs and performing ascending arrangement on the marked application programs according to the preference values;
the keyword determining unit is used for sequentially acquiring search records and determining keywords according to the search records;
and the answer analysis unit is used for reading the risk questions according to the keywords, displaying the risk questions at regular time, receiving feedback answers and verifying the identity of the user according to the feedback answers.
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| CN106980497A (en) * | 2017-02-10 | 2017-07-25 | 九次方大数据信息集团有限公司 | Webpage and website performance optimization method and device |
| CN109726087A (en) * | 2018-04-13 | 2019-05-07 | 平安普惠企业管理有限公司 | Multiple web pages scheme test method, device, equipment and computer readable storage medium |
| CN111198797A (en) * | 2019-12-27 | 2020-05-26 | 华为技术有限公司 | Operation monitoring method and device and operation analysis method and device |
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| CN113537825A (en) * | 2021-08-03 | 2021-10-22 | 国网青海省电力公司信息通信公司 | Operation and maintenance state monitoring and processing method, system, device and storage medium |
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| CN106980497A (en) * | 2017-02-10 | 2017-07-25 | 九次方大数据信息集团有限公司 | Webpage and website performance optimization method and device |
| CN109726087A (en) * | 2018-04-13 | 2019-05-07 | 平安普惠企业管理有限公司 | Multiple web pages scheme test method, device, equipment and computer readable storage medium |
| CN111198797A (en) * | 2019-12-27 | 2020-05-26 | 华为技术有限公司 | Operation monitoring method and device and operation analysis method and device |
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