CN115292301B - Task data abnormity monitoring and processing method and system based on artificial intelligence - Google Patents
Task data abnormity monitoring and processing method and system based on artificial intelligence Download PDFInfo
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Abstract
According to the task data anomaly monitoring processing method and system based on artificial intelligence, the difference vector of the positioning of the corresponding characters in the first office task data and the second office task data is determined by determining the positioning of the corresponding characters in the first office task data and the second office task data. And optimizing the office task data monitoring range corresponding to the target monitoring range in the first office task data by using the difference vector. The aim of optimizing the office task data monitoring range in the office task data is fulfilled. Therefore, the problem of excessive abnormal data processing time when the office task data monitoring range in the office task data is optimized in the prior art can be solved, and the accuracy and reliability of the abnormal monitoring processing of the optimized task data can be improved.
Description
Technical Field
The application relates to the technical field of abnormal data monitoring, in particular to a task data abnormal monitoring processing method and system based on artificial intelligence.
Background
Artificial intelligence (Artificial Intelligence) is a new technical Science based on Computer Science (Computer Science), which is a cross-discipline, emerging discipline, cross-fused by multiple disciplines such as Computer, psychology, philosophy, etc., to research, develop theories, methods, techniques and application systems for simulating, extending and expanding human intelligence, to attempt to understand the essence of the intelligence, and to produce a new intelligent machine that can react in a similar manner to human intelligence, and research in this field includes robotics, language recognition, image recognition, natural language processing, expert systems, etc.
With the continuous development of internet technology, the information volume is continuously increased, so that the frequency of data abnormality is more. In the conventional technology, abnormal data is monitored by a person, thus, compared with the case where only the person is wasted, the problem of missing monitoring of the abnormal data may also occur.
Disclosure of Invention
In order to improve the technical problems in the related art, the application provides a task data abnormity monitoring and processing method and system based on artificial intelligence.
In a first aspect, a task data anomaly monitoring and processing method based on artificial intelligence is provided, and the method at least includes: the method comprises the steps of obtaining first office task data obtained by recording a specified monitoring range by a monitoring system, wherein the first office task data comprises a target monitoring range in the specified monitoring range; determining the positioning of X characters of the target monitoring range in the first office task data to obtain X positioning, wherein X is an integer greater than or equal to 2; determining the positioning of the designated character corresponding to each character in the X characters in second office task data to obtain X designated positioning, wherein the second office task data is office task data obtained by recording the designated monitoring range by the monitoring system; determining difference vectors between each positioning and each appointed positioning to obtain X difference vectors; and optimizing the characters of the target monitoring range in the first office task data according to the X difference vectors to obtain target office task data.
In an independent embodiment, determining the location of the target monitoring range in the X characters in the first office task data, to obtain the X locations includes: determining a first office task data monitoring range of the target monitoring range in the first office task data; determining X vertexes in the first office task data monitoring range as X characters; and determining the positioning of the X characters in a designated office task data positioning relation network to obtain the X positioning.
In an independent embodiment, determining the location of the designated character corresponding to each of the X characters in the second office task data to obtain X designated locations includes: marking each of the designated characters matching each of the characters in the second office task data; and determining the positioning of each appointed character in an appointed office task data positioning relation network to obtain the X appointed positioning.
In an independently implemented embodiment, determining a difference vector between each of the locations and each of the specified locations results in X difference vectors, comprising: determining a feature commonality vector between the first office task data and the second office task data; on the basis that the feature commonality vector between the first office task data and the second office task data is not smaller than a first specified judgment value, determining each positioning and migration monitoring ranges between the specified positioning to obtain X migration monitoring ranges; and combining the number of office tasks included in each migration monitoring range to obtain the X difference vectors.
In an independently implemented embodiment, determining a feature commonality vector between the first office task data and the second office task data comprises: dividing the first office task data into Y office task data monitoring ranges, wherein Y is an integer greater than or equal to 2; determining a second office task data monitoring range from the Y office task data monitoring ranges; comparing the office tasks in the second office task data monitoring range with the office tasks in the office task data monitoring range of the corresponding characters in the second office task data; and determining a feature commonality vector between the office tasks in the second office task data monitoring range and the office tasks in the office task data monitoring range of corresponding characters in the second office task data as the feature commonality vector between the first office task data and the second office task data.
In an independently implemented embodiment, the method further comprises: and on the basis that the feature commonality vector between the first office task data and the second office task data is smaller than the first specified judgment value, obtaining third office task data obtained by recording the specified monitoring range by the monitoring system in a specified time period after the first office task data is obtained.
In an independent embodiment, optimizing the characters of the target monitoring range in the first office task data according to the X positioning difference vectors to obtain target office task data includes: determining a mapping relation between the first office task data and the second office task data through the X positioning difference vectors; and optimizing the characters of the target monitoring range in the first office task data according to the mapping relation to obtain target office task data.
In an independently implemented embodiment, determining a mapping relationship between the first office task data and the second office task data by the X positioning disparity vectors includes: determining a first office task data vector of the first office task data and a second office task data vector of the second office task data; and registering the first office task data vector and the second office task data vector according to the positioning difference vectors to obtain the mapping relation.
In an independent embodiment, optimizing the characters of the target monitoring range in the first office task data according to the mapping relation to obtain target office task data includes: determining the spatial location of each character according to the attribute in the mapping relation and each location; and optimizing the characters of the target monitoring range in the first office task data by simplifying the space positioning of each character and each positioning, so as to obtain the target office task data.
In an independent embodiment, after optimizing the characters of the target monitoring range in the first office task data according to the mapping relationship to obtain target office task data, the method further includes: and optimizing the second office task data according to the first office task data on the basis that the feature commonality vector between the first office task data and the second office task data is not smaller than a second determination value.
In an independent embodiment, after optimizing the characters of the target monitoring range in the first office task data according to the X difference vectors to obtain target office task data, the method further includes: determining optimized X characters in the target office task data; and splicing the optimized X characters in the target office task data to obtain an optimized first office task data monitoring range, wherein the first office task data monitoring range is an office task data monitoring range of the target monitoring range in the first office task data.
In a second aspect, an artificial intelligence based task data anomaly monitoring processing system is provided, including a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method described above.
According to the task data anomaly monitoring processing method and system based on the artificial intelligence, the difference vector of the positioning of the corresponding characters in the first office task data and the second office task data is determined by determining the positioning of the corresponding characters in the first office task data and the second office task data. And optimizing the office task data monitoring range corresponding to the target monitoring range in the first office task data by using the difference vector. The aim of optimizing the office task data monitoring range in the office task data is fulfilled. Therefore, the problem of excessive abnormal data processing time when the office task data monitoring range in the office task data is optimized in the prior art can be solved, and the accuracy and reliability of the office task data can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a task data anomaly monitoring processing method based on artificial intelligence according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions described above, the following detailed description of the technical solutions of the present application is provided through the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limit the technical solutions of the present application, and the technical features of the embodiments and embodiments of the present application may be combined with each other without conflict.
Referring to fig. 1, a task data anomaly monitoring processing method based on artificial intelligence is shown, and the method may include the following technical solutions described in step S202-step S210.
Step S202, first office task data obtained by recording the specified monitoring range by the monitoring system is obtained, wherein the first office task data comprises a target monitoring range in the specified monitoring range.
In this embodiment, the specified monitoring range may be a random one monitoring range, and the target monitoring range is a random one monitoring range among the specified monitoring ranges.
Step S204, determining the positioning of X characters in the first office task data in the target monitoring range to obtain X positioning, wherein X is an integer greater than or equal to 2.
Step S206, determining the positioning of the designated character corresponding to each character in the X characters in the second office task data to obtain X designated positioning, wherein the second office task data is office task data obtained by recording a designated monitoring range by the monitoring system;
in this embodiment, the second office task data may be office task data after optimization, or may be original office task data, that is, the office task data monitoring range included therein corresponds to characters of a target monitoring range in reality.
Step S208, determining difference vectors between each positioning and each appointed positioning, and obtaining X difference vectors.
Step S210, optimizing characters in the first office task data in the target monitoring range by using the X difference vectors to obtain target office task data.
In this embodiment, the difference vector is compensated to the office task data monitoring range where migration occurs, so that the office task data monitoring range can be effectively optimized.
Through the steps, the difference vector of the positioning of the corresponding characters in the first office task data and the second office task data is determined by determining the positioning of the corresponding characters in the first office task data and the second office task data. And optimizing the office task data monitoring range corresponding to the target monitoring range in the first office task data by using the difference vector. The aim of optimizing the office task data monitoring range in the office task data is fulfilled. Therefore, the problem of excessive abnormal data processing time when the office task data monitoring range in the office task data is optimized in the prior art can be solved, and the accuracy and reliability of the office task data can be improved.
For some possible embodiments, determining the location of the target monitoring range in the X characters in the first office task data, and obtaining the X locations may specifically include the following.
S11, determining a first office task data monitoring range of the target monitoring range in the first office task data.
And S12, determining X vertexes in the first office task data monitoring range as X characters.
S13, determining the positioning of X characters in the designated office task data positioning relation network to obtain X positioning.
For some possible embodiments, determining the location of the specified character corresponding to each of the X characters in the second office task data to obtain the X specified locations may specifically include the following steps.
S21, marking each designated character matched with each character in the second office task data.
S22, determining the positioning of each appointed character in the appointed office task data positioning relation network to obtain X appointed positioning.
For some possible embodiments, the difference vector between each location and each specified location is determined, resulting in X difference vectors, which may specifically include the following steps.
S31, determining a feature commonality vector between the first office task data and the second office task data.
S32, determining each positioning and migration monitoring ranges among the designated positioning on the basis that the feature commonality vector between the first office task data and the second office task data is not smaller than a first designated judgment value, and obtaining X migration monitoring ranges.
S33, combining the number of office tasks included in each migration monitoring range to obtain X difference vectors.
In the present embodiment, the feature commonality vector between the first office task data and the second office task data is determined by: dividing the first office task data into Y office task data monitoring ranges, wherein Y is an integer greater than or equal to 2; determining a second office task data monitoring range from the Y office task data monitoring ranges; comparing the office tasks in the second office task data monitoring range with the office tasks in the office task data monitoring range of the corresponding characters in the second office task data; and determining the feature commonality vector between the office tasks in the second office task data monitoring range and the office tasks in the office task data monitoring range of the corresponding characters in the second office task data as the feature commonality vector between the first office task data and the second office task data.
For some possible implementations, the method may further include the following steps.
S41, on the basis that the feature commonality vector between the first office task data and the second office task data is smaller than a first specified judgment value, third office task data obtained by recording a specified monitoring range by a monitoring system is obtained in a specified time period after the first office task data is obtained.
In this embodiment, on the basis that the correlation between the first office task data and the second office task data is lower than the first specified determination value, it means that the difference between the first office task data and the second office task data is relatively large.
For some possible embodiments, the characters in the first office task data in the target monitoring range are optimized by using the X positioning difference vectors, so as to obtain target office task data, which may specifically include the following.
S51, determining a mapping relation between the first office task data and the second office task data based on the X positioning difference vectors.
And S52, optimizing characters of the target monitoring range in the first office task data by using the mapping relation to obtain target office task data.
In the present embodiment, the mapping relationship between the first office task data and the second office task data is determined based on the X positioning difference vectors by: determining a first office task data vector of the first office task data and a second office task data vector of the second office task data; and matching the first office task data vector and the second office task data vector according to each positioning difference vector to obtain a mapping relation.
For some possible embodiments, the mapping relationship is used to optimize the characters in the first office task data in the target monitoring range, so as to obtain target office task data, which may specifically include the following.
S61, determining the space positioning of each character by using the attribute and each positioning in the mapping relation.
S62, optimizing characters in the first office task data in the target monitoring range by simplifying the space positioning and the positioning of each character, and obtaining target office task data.
For some possible embodiments, the following steps may be further included after the characters in the first office task data in the target monitoring range are optimized by using the mapping relationship to obtain the target office task data.
S71, updating the second office task data by using the first office task data on the basis that the feature commonality vector between the first office task data and the second office task data is not smaller than a second determination value.
In this embodiment, optimizing the first office task data may reduce interference of the data.
For some possible embodiments, the following steps may be further included after optimizing the characters in the first office task data for the target monitoring range using the X difference vectors to obtain the target office task data.
S81, determining the optimized X characters in the target office task data.
And S82, splicing the optimized X characters in the target office task data to obtain an optimized first office task data monitoring range, wherein the first office task data monitoring range is an office task data monitoring range of which the target monitoring range is in the first office task data.
On the basis of the above, there is provided an artificial intelligence based task data anomaly monitoring processing apparatus 200 applied to an artificial intelligence based task data anomaly monitoring processing system, the apparatus comprising:
the task obtaining module 210 is configured to obtain first office task data obtained by recording a specified monitoring range by a monitoring system, where the first office task data includes a target monitoring range in the specified monitoring range;
the positioning determining module 220 is configured to determine the positioning of X characters in the first office task data in the target monitoring range, to obtain X positioning, where X is an integer greater than or equal to 2;
the positioning designating module 230 is configured to determine the positioning of designated characters corresponding to each of the X characters in second office task data, to obtain X designated positioning, where the second office task data is office task data obtained by recording the designated monitoring range by the monitoring system;
a data determining module 240, configured to determine a difference vector between each of the locations and each of the specified locations, to obtain X difference vectors; and optimizing the characters of the target monitoring range in the first office task data according to the X difference vectors to obtain target office task data.
Based on the above, an artificial intelligence based task data anomaly monitoring processing system 300 is shown, comprising a processor 310 and a memory 320 in communication with each other, the processor 310 being configured to read a computer program from the memory 320 and execute the computer program to implement the method described above.
On the basis of the above, there is also provided a computer readable storage medium on which a computer program stored which, when run, implements the above method.
In summary, based on the above scheme, by determining the positions of the corresponding characters in the first office task data and the second office task data, a difference vector of the positions of the corresponding characters in the first office task data and the second office task data is determined. And optimizing the office task data monitoring range corresponding to the target monitoring range in the first office task data by using the difference vector. The aim of optimizing the office task data monitoring range in the office task data is fulfilled. Therefore, the problem of excessive abnormal data processing time when the office task data monitoring range in the office task data is optimized in the prior art can be solved, and the accuracy and reliability of the office task data can be improved.
It should be appreciated that the systems and modules thereof shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only with hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software, such as executed by various types of processors, and with a combination of the above hardware circuitry and software (e.g., firmware).
It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations of the present application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the invention are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application and are not intended to limit the order in which the processes and methods of the application are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed herein and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the numbers allow for adaptive variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this application is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the present application, documents that are currently or later attached to this application for which the broadest scope of the claims to the present application is limited. It is noted that the descriptions, definitions, and/or terms used in the subject matter of this application are subject to such descriptions, definitions, and/or terms if they are inconsistent or conflicting with such descriptions, definitions, and/or terms.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of this application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present application may be considered in keeping with the teachings of the present application. Accordingly, embodiments of the present application are not limited to only the embodiments explicitly described and depicted herein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (2)
1. The task data anomaly monitoring and processing method based on artificial intelligence is characterized by comprising the following steps of:
the method comprises the steps of obtaining first office task data obtained by recording a specified monitoring range by a monitoring system, wherein the first office task data comprises a target monitoring range in the specified monitoring range;
determining the positioning of X characters of the target monitoring range in the first office task data to obtain X positioning, wherein X is an integer greater than or equal to 2;
determining the positioning of the designated character corresponding to each character in the X characters in second office task data to obtain X designated positioning, wherein the second office task data is office task data obtained by recording the designated monitoring range by the monitoring system;
determining difference vectors between each positioning and each appointed positioning to obtain X difference vectors; optimizing characters of the target monitoring range in the first office task data according to the X difference vectors to obtain target office task data;
determining the positioning of the target monitoring range in X characters in the first office task data to obtain X positioning, wherein the positioning comprises the following steps:
determining a first office task data monitoring range of the target monitoring range in the first office task data;
determining X vertexes in the first office task data monitoring range as X characters;
determining the positioning of the X characters in a designated office task data positioning relation network to obtain the X positioning;
determining the location of the designated character corresponding to each of the X characters in the second office task data to obtain X designated locations, including:
marking each of the designated characters matching each of the characters in the second office task data;
determining the location of each appointed character in an appointed office task data location relation network to obtain X appointed locations;
determining a difference vector between each of the locations and each of the specified locations to obtain X difference vectors, including:
determining a feature commonality vector between the first office task data and the second office task data;
on the basis that the feature commonality vector between the first office task data and the second office task data is not smaller than a first specified judgment value, determining each positioning and migration monitoring ranges between the specified positioning to obtain X migration monitoring ranges;
combining the number of office tasks included in each migration monitoring range to obtain the X difference vectors;
determining a feature commonality vector between the first office task data and the second office task data, comprising:
dividing the first office task data into Y office task data monitoring ranges, wherein Y is an integer greater than or equal to 2; determining a second office task data monitoring range from the Y office task data monitoring ranges;
comparing the office tasks in the second office task data monitoring range with the office tasks in the office task data monitoring range of the corresponding characters in the second office task data;
determining a feature commonality vector between the office tasks in the second office task data monitoring range and the office tasks in the office task data monitoring range of corresponding characters in the second office task data as a feature commonality vector between the first office task data and the second office task data;
the method further comprises the steps of: on the basis that the feature commonality vector between the first office task data and the second office task data is smaller than the first specified judgment value, third office task data obtained by recording the specified monitoring range by the monitoring system is obtained in a specified time period after the first office task data is obtained;
optimizing the characters of the target monitoring range in the first office task data according to the X difference vectors to obtain target office task data, wherein the method comprises the following steps:
determining a mapping relation between the first office task data and the second office task data through the X difference vectors;
optimizing characters of the target monitoring range in the first office task data according to the mapping relation to obtain target office task data;
wherein determining, by the X disparity vectors, a mapping relationship between the first office task data and the second office task data includes:
determining a first office task data vector of the first office task data and a second office task data vector of the second office task data;
registering the first office task data vector and the second office task data vector according to the difference vectors to obtain the mapping relation;
the optimizing the characters of the target monitoring range in the first office task data according to the mapping relation to obtain target office task data comprises the following steps:
determining the spatial location of each character according to the attribute in the mapping relation and each location;
optimizing the characters of the target monitoring range in the first office task data by simplifying the space positioning of each character and each positioning to obtain the target office task data;
optimizing characters of the target monitoring range in the first office task data according to the mapping relation to obtain target office task data, wherein the method further comprises the following steps: optimizing the second office task data according to the first office task data on the basis that the feature commonality vector between the first office task data and the second office task data is not smaller than a second determination value;
optimizing characters of the target monitoring range in the first office task data according to the X difference vectors to obtain target office task data, wherein the method further comprises the following steps: determining optimized X characters in the target office task data; and splicing the optimized X characters in the target office task data to obtain an optimized first office task data monitoring range, wherein the first office task data monitoring range is an office task data monitoring range of the target monitoring range in the first office task data.
2. An artificial intelligence based task data anomaly monitoring processing system comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of claim 1.
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