CN115170270A - Data detection method and system based on big data behavior analysis - Google Patents
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Abstract
Description
Technical Field
The invention relates to the field of data processing, in particular to a data detection method and system based on big data behavior analysis.
Background
With the continuous updating of information technology and the wide application of computer science and technology, all industries enter a big data era, the recording, accounting and economic business management of massive electronic data become inevitable ways, and the traditional account book type and manual type data detection cannot meet the detection requirements.
The document with the application number of 201910912011.x discloses a network mode internal auditing system, and a network mode internal auditing method applied by the network mode internal auditing system comprises the following steps: the company leader group in the internal audit result application network module organizes audit decisions, the audit decision information flow is communicated to the internal audit service management network module, and an audit committee in the internal audit service management network module issues an audit task according to the audit decisions; the members of the audit team cooperate with the personnel in an audit team member stock base of the internal audit team building network module to establish the audit team; and after receiving the audit task, the audit group uses an internal audit information platform network module to conduct data export, analysis, arrangement and online audit and input problem item information, forms an audit report and an rectification report after online confirmation, and finally feeds the information flow of the audit report and the rectification report back to the company leader group and subordinate units.
In the prior art, an audit group is established through a published audit task to carry out data export, analysis, arrangement and online detection, and when the data export is carried out, the data volume needing to be processed is large, and the detection efficiency is not high.
Disclosure of Invention
Therefore, the invention provides a data detection method and system based on big data behavior analysis, which can solve the problem of low detection efficiency.
In order to achieve the above object, the present invention provides a data detection method based on big data behavior analysis, the method comprising:
generating different types of detection lists according to the detection requirements of the terminal data, wherein the detection lists comprise detection data names and detection periods;
distributing a preset number of first-level detection personnel and second-level detection personnel according to the duration of the detection period, wherein the first-level detection personnel are used for carrying out initial detection on detection data, and the second-level detection personnel are used for rechecking the detection data;
calling data to be detected of each terminal in a detection period according to the detection data name to obtain a first detection data list, wherein the data to be detected comprises data volume;
the called data to be detected are sorted according to the data quantity of the data to be detected, and a second detection data list is obtained;
and the second detection data list is used as the detection sequence of the primary detection personnel and the secondary detection personnel for performing initial detection and rechecking.
Further, when detecting personnel are distributed, time period average division is carried out on the duration of the detection period, a preset number of primary detecting personnel and secondary detecting personnel are distributed to each time period, the number of the divided time periods is determined by the duration of the detection period, a first period and a second period are set, the detection period is compared with the first period and the second period, and when the duration of the detection period is larger than or equal to the second period, the duration of the detection period is divided into 3 time periods; when the time length of the detection period is less than or equal to the first period and less than the second period, dividing the time length of the detection period into 2 time periods; and when the duration of the detection period is less than the first period, dividing the duration of the detection period into 1 time segment.
Further, when the data to be detected is called, the related data to be detected in the detection period is called from each terminal according to the detection data name, if the detection data name is A, the data of each terminal is associated with the detection data name A, the obtained associated data is subjected to time screening, the data in the detection period is screened out, and sorting is performed according to time, so that a first detection data list is obtained.
Further, when the data to be detected are sorted, the data to be detected in the first detection data list are subjected to priority sorting, the data to be detected comprise a data volume and a terminal name, if the terminal names corresponding to the data to be detected are different, the data to be detected in the first detection data list are subjected to priority sorting according to the data volume, the larger the data volume is, the higher the priority sorting of the corresponding data to be detected is, if the data to be detected in the first detection data list are respectively the data volume A1 and the corresponding terminal C1, the data volume A2 and the corresponding terminal C2,
when the data volume A1 is larger than the data volume A2, the priority of the data to be detected of the terminal C1 is arranged in front of the priority of the data to be detected of the terminal C2;
when the data volume A1= the data volume A2, the priority of the data to be detected of the terminal C1 and the priority of the data to be detected of the terminal C1 are sorted in the original order;
and when the data volume A1 is less than the data volume A2, the priority of the data to be detected of the terminal C2 is ranked behind the priority of the data to be detected of the terminal C1.
Further, when the data to be detected are sorted, if the names of the terminals corresponding to the data to be detected are the same, the data volume of each terminal is calculated to determine the priority sorting sequence of the data to be detected, if three data to be detected belong to the terminal C1, the data volumes of the data to be detected are A1, A2 and A3 respectively, two data to be detected belong to the terminal C2 respectively, the data volumes of the data to be detected are A4 and A5 respectively,
calculating the sum of the data volume of the terminal C1 and the data volume of the terminal C2 to obtain that the sum of the data volume of the terminal C1 is A1+ A2+ A3= E1; the sum of the data amount of the terminal C2 is A4+ A5= E2;
comparing the data volume of the terminal C1 with the data volume E1 of the terminal C1 and the data volume of the terminal C2 with the data volume E2, and when the data volume E1 is larger than the data volume E2, ranking the priority of the data to be detected belonging to the terminal C1 before the priority of the data to be detected belonging to the terminal C2;
when the data volume sum E1= the data volume sum E2, the priority of the data to be detected belonging to the terminal C1 and the priority of the data to be detected belonging to the terminal C2 are sorted in the original order;
when the data amount sum E1 is less than the data amount sum E2, the priority of the data to be detected belonging to the terminal C2 is ranked before the priority of the data to be detected belonging to the terminal C1.
Further, the invention provides a data detection method based on big data behavior analysis, and the method further comprises the following steps: and performing initial detection and rechecking on the data to be detected in the second detection data list in sequence to obtain a detection result, wherein the detection result is whether the data to be detected is abnormal or not, if so, the reason of the abnormality is obtained, and the detection result is transmitted to the corresponding terminal.
Further, the invention provides a data detection method based on big data behavior analysis, and the method further comprises the following steps: evaluating the detection result formed by detection, evaluating according to the reason type of the abnormality in the detection result to obtain an evaluation result and a corrective measure, and transmitting the evaluation result and the corrective measure to a corresponding terminal:
if the reason category relates to data abnormity, evaluating the grade of the reason category as A grade;
if the reason type relates to abnormal credential information of the data, evaluating the level of the data as B level;
if the reason category relates to detection of flow abnormality, the grade of the reason category is evaluated as grade C;
wherein, the grade A is more than the grade B is more than the grade C, and corresponding corrective measures are given according to the evaluation grade.
Further, the invention also provides a data detection system based on big data behavior analysis, which comprises:
the generating module is used for generating different types of detection lists according to the detection requirements of the terminal detection data, and the detection lists comprise detection data names and detection periods;
the distribution module is used for distributing a preset number of first-level detection personnel and second-level detection personnel according to the duration of the detection period, wherein the first-level detection personnel is used for carrying out initial detection on detection data, and the second-level detection personnel is used for rechecking the detection data;
the calling module is used for calling to-be-detected data of each terminal in a detection period according to the detection data name to obtain a first detection data list, wherein the to-be-detected data comprises data volume;
the sorting module is used for sorting the called data to be detected according to the data quantity of the data to be detected to obtain a second detection data list;
and the second detection data list is used as a detection sequence for the primary detection personnel and the secondary detection personnel to carry out initial detection and recheck.
The system further comprises a detection module and an evaluation module, wherein the detection module is used for carrying out initial detection and rechecking on the data to be detected in the second detection data list according to the sequence to obtain a detection result, and the evaluation module is used for evaluating the detection result formed by detection to obtain an evaluation result and a correction measure.
Further, the detection module performs time period average division on the duration of the detection period when detecting personnel are distributed, and distributes a preset number of primary detecting personnel and secondary detecting personnel for each time period, the number of the divided time periods is determined by the duration of the detection period, wherein a first period and a second period are arranged, the detection period is compared with the first period and the second period, and when the duration of the detection period is larger than or equal to the second period, the duration of the detection period is divided into 3 time periods; when the time length of the detection period is less than or equal to the first period and less than the second period, dividing the time length of the detection period into 2 time periods; and when the duration of the detection period is less than the first period, dividing the duration of the detection period into 1 time segment.
Compared with the prior art, the method has the advantages that the preset number of first-level detection personnel and second-level detection personnel are distributed to the detection data in the detection list according to the duration of the detection period, then the to-be-detected data of each terminal are called according to the detection data names, due to the fact that the to-be-detected data are large in quantity, the called to-be-detected data are sorted according to the data quantity of the to-be-detected data, the larger the data quantity of the to-be-detected data is, the higher the priority of the to-be-detected data is, the to-be-detected data with high priority are preferentially detected, and detection efficiency is improved.
Particularly, time periods are divided according to the duration of the detection period, a preset number of first-level detection personnel and second-level detection personnel are allocated to each time period, and the second-level detection personnel recheck on the basis of detection of the first-level detection personnel, so that the detection quality and safety are improved.
Particularly, the data of each terminal is associated with the detection data name A, the obtained associated data is subjected to time screening, the data in the detection period is screened out and sorted according to time to obtain a first detection data list, the detection data name is associated with each row of data, useful data are accurately called from a large amount of data, the accuracy of the data to be detected is improved, and the detection efficiency is further improved.
Particularly, if the terminal names corresponding to the data to be detected are different, the data to be detected in the first detection data list are subjected to priority ranking according to the data quantity involved in the data to be detected, and if the data quantity is larger, the priority ranking of the data to be detected is higher, the data to be detected is preferentially detected, so that the detection quality and the detection efficiency are improved.
Particularly, when the data to be detected are sequenced, if the names of the terminals corresponding to the data to be detected are the same, the data volume of each terminal is calculated, the priority sequencing sequence of the data to be detected is determined, the larger the data volume sum of the terminals is, the higher the priority sequencing sequence of the data to be detected of the terminals is, all the data to be detected belonging to the terminal are preferentially detected, and therefore the detection quality and the detection efficiency are improved.
Particularly, the first detection and the second detection are respectively carried out according to the priority sorting sequence in the second detection data list, whether the abnormality exists is analyzed through two times of detection, if the abnormality exists, the reason of the abnormality exists is given, then the final detection result is obtained, the quality of the detection data is improved, and finally the detection result is transmitted to the corresponding terminal, so that the effective intercommunication of the data is realized.
Particularly, when the detected data is abnormal, the reason and the category of the abnormal data are evaluated, the corrective measures are obtained through different risk levels, the evaluation result and the corrective measures are transmitted to the corresponding terminal, the authenticity of the terminal data is guaranteed, and the safety of the terminal data is improved.
Particularly, the preset number of first-level detection personnel and second-level detection personnel are distributed to the detection data in the detection list generated by the generation module through the distribution module according to the duration of the detection period, then the calling module calls the data to be detected of each terminal according to the name of the detection data, because the data quantity to be detected is huge, the calling module sorts the data to be detected according to the data quantity of the data to be detected, the larger the data quantity of the data to be detected is, the higher the priority of the data to be detected is, the data to be detected with high priority is preferentially detected, and the detection efficiency is improved.
Particularly, the detection module performs initial detection and rechecking on the data to be detected in the second detection data list in sequence to obtain a detection result, the detection result is transmitted to the corresponding terminal to realize effective intercommunication of the data, the evaluation module evaluates the detection data with abnormality to obtain an evaluation result and transmits correction measures to the corresponding terminal to enable the evaluation result and the correction measures to be taken, and the quality of the data is effectively improved.
Drawings
Fig. 1 is a schematic flowchart of a data detection method based on big data behavior analysis according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data detection system based on big data behavior analysis according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and do not delimit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a data detection method based on big data behavior analysis according to an embodiment of the present invention includes:
step S110, generating different types of detection lists according to the detection requirements of the terminal detection data, wherein the detection lists comprise detection data names and detection periods;
step S120, distributing a preset number of first-level detection personnel and second-level detection personnel according to the duration of the detection period, wherein the first-level detection personnel is used for carrying out initial detection on detection data, and the second-level detection personnel is used for rechecking the detection data;
step S130, calling data to be detected of each terminal in a detection period according to the detection data name to obtain a first detection data list, wherein the data to be detected comprises data volume;
step S140, sorting the called data to be detected according to the data quantity of the data to be detected to obtain a second detection data list;
and the second detection data list is used as a detection sequence for the primary detection personnel and the secondary detection personnel to carry out initial detection and recheck.
Specifically, the application scenario of the method can be that data of each bank terminal is audited, namely, an audit list of each department is generated according to audit requirements of decisions in banks, a preset number of first-level auditors and second-level auditors are distributed to audit objects according to duration of a preset audit period in the audit list for two audits, after the auditors are distributed, corresponding data to be audited are dispatched to a head office, a branch office and a branch office according to names of the audit objects, due to the fact that the quantity of the data to be audited is large, the dispatched data to be audited are sequenced according to the amount of the data to be audited, the larger the amount of the data to be audited is, the higher the priority of the data to be auditors is, and then the auditors conduct two audits according to the audit sequence.
Specifically, according to the embodiment of the invention, a preset number of first-level detection personnel and second-level detection personnel are allocated to detection data in a detection list according to the duration of a detection period, and then the to-be-detected data of each terminal is called according to the name of the detection data.
Specifically, when detecting personnel are distributed, time period average division is carried out on the time length of a detection period, a preset number of primary detecting personnel and secondary detecting personnel are distributed to each time period, the number of the divided time periods is determined by the time length of the detection period, a first period and a second period are arranged, the detection period is compared with the first period and the second period, and when the time length of the detection period is larger than or equal to the second period, the time length of the detection period is divided into 3 time periods; when the first period is less than or equal to the detection period and less than the second period, dividing the duration of the detection period into 2 time periods; and when the duration of the detection period is less than the first period, dividing the duration of the detection period into 1 time segment.
Specifically, taking a bank data audit meter as an example, the bank data audit meter is averagely divided into a time period t1, a time period t2 and a time period t3 according to the duration of an audit period, the number of first-level auditors respectively allocated to the time period t1, the time period t2 and the time period t3 is a, the number of second-level auditors is b, and the number a is greater than the number b.
Specifically, the embodiment of the invention divides the time period of the detection period, allocates the preset number of the first-level detection personnel and the second-level detection personnel to each time period, and rechecks the second-level detection personnel on the basis of the detection of the first-level detection personnel, thereby improving the detection quality and safety.
Specifically, when the data to be detected is called, the data to be detected in the detection period is called from each terminal according to the name of the detection data, if the name of the detection data is A, the data of each terminal is associated with the name of the detection data A, the obtained associated data is subjected to time screening, the data in the detection period is screened out, and sorting is performed according to time, so that a first detection data list is obtained.
Specifically, taking the bank data audit as an example, if the name of the audit object is a deposit business and the audit period is 2022.5.1 to 2022.5.31, then data associated with the name of the audit object as a deposit business, such as a fixed deposit or a current deposit, including the flow operation of the deposit business, the deposit amount and the related certificate, is called from the data of the head office, the branch office and the branch office, and then the associated data is screened out to screen out the data of the period from 2022.5.1 to 2022.5.31, so as to form a first audit data list.
Specifically, the data of each terminal is associated with the detection data name A, the obtained associated data is subjected to time screening, the data in the detection period is screened out and sorted according to time to obtain a first detection data list, the detection data names are associated with the data of each row, useful data are accurately called from a large amount of data, the accuracy of the data to be detected is improved, and the detection efficiency is improved.
Specifically, when data to be detected are sequenced, data to be detected in a first detection data list are subjected to priority sequencing, the data to be detected comprise a data volume and a terminal name, if the terminal names corresponding to the data to be detected are different, the data to be detected in the first detection data list are subjected to priority sequencing according to the data volume, the larger the data volume is, the higher the priority sequencing of the corresponding data to be detected is, and if the data to be detected in the first detection data list are respectively a data volume A1 and a terminal C1 corresponding to the data volume A1, a data volume A2 and a terminal C2 corresponding to the data volume A1;
when the data volume A1 is larger than the data volume A2, the priority of the data to be detected of the terminal C1 is arranged in front of the priority of the data to be detected of the terminal C2;
when the data volume A1= the data volume A2, the priority of the data to be detected of the terminal C1 and the priority of the data to be detected of the terminal C1 are sorted in the original order;
and when the data volume A1 is less than the data volume A2, the priority of the data to be detected of the terminal C2 is ranked behind the priority of the data to be detected of the terminal C1.
Specifically, according to the embodiment of the present invention, if the names of the terminals corresponding to the data to be detected are different, the data to be detected in the first detection data list is prioritized according to the data amount involved in the data to be detected, and if the data amount is larger, the prioritized data in the first detection data list is higher, the data to be detected is detected preferentially, so that the detection quality and the detection efficiency are improved.
Specifically, when data to be detected are sequenced, if the names of terminals corresponding to the data to be detected are the same, the data volume of each terminal is calculated and the priority sequencing sequence of the data to be detected is determined, if three data to be detected belong to the terminal C1, the data volumes of the data to be detected are A1, A2 and A3 respectively, two data to be detected belong to the terminal C2 respectively, and the data volumes of the data to be detected are A4 and A5 respectively;
calculating the sum of the data volume of the terminal C1 and the data volume of the terminal C2 to obtain that the sum of the data volume of the terminal C1 is A1+ A2+ A3= E1; the sum of the data amount of the terminal C2 is A4+ A5= E2;
comparing the data volume of the terminal C1 with the data volume E1 of the terminal C2 with the data volume E2 of the terminal C2, and when the data volume E1 is larger than the data volume E2, ranking the priority of the data to be detected belonging to the terminal C1 before the priority of the data to be detected belonging to the terminal C2;
when the data volume sum E1= the data volume sum E2, the priority of the data to be detected belonging to the terminal C1 and the priority of the data to be detected belonging to the terminal C2 are sorted in the original order;
when the data amount sum E1 is less than the data amount sum E2, the priority of the data to be detected belonging to the terminal C2 is ranked before the priority of the data to be detected belonging to the terminal C1.
Specifically, taking a bank data audit as an example, when the sum of the money amount of the branch bank C1 is greater than the sum of the money amount of the branch bank C2, all the data to be processed belonging to the branch bank C1 are preferentially calculated, the sorting of the data to be audited belonging to the branch bank C1 is performed according to the size of the money amount of the data to be audited, and the sorting method of the data to be audited belonging to the branch bank C2 is consistent with the sorting method of the data to be audited belonging to the branch bank C1.
Specifically, in the embodiment of the invention, when the data to be detected is sequenced, if the names of the terminals corresponding to the data to be detected are the same, the data volume of each terminal is calculated and the priority sequencing sequence of the data to be detected is determined, and the larger the data volume sum of the terminals is, the higher the priority sequencing sequence of the data to be detected of the terminal is, the detection of all the data to be detected belonging to the terminal is preferentially performed, so that the detection quality and the detection efficiency are improved.
Specifically, referring to fig. 1, the data detection method based on big data behavior analysis according to the embodiment of the present invention further includes:
and S150, performing initial detection and rechecking on the data to be detected in the second detection data list in sequence to obtain a detection result, wherein the detection result is whether the data to be detected is abnormal or not, if so, the reason of the abnormality is obtained, and the detection result is transmitted to a corresponding terminal.
Specifically, the embodiment of the invention respectively performs the initial check and the recheck according to the priority sorting sequence in the second detection data list, analyzes whether the abnormality exists after two times of detection, gives the reason of the abnormality if the abnormality exists, then obtains the final detection result, improves the quality of the detection data, and finally transmits the detection result to the corresponding terminal, thereby realizing the effective intercommunication of the data.
Specifically, referring to fig. 1, the data detection method based on big data behavior analysis according to the embodiment of the present invention further includes:
step S160, evaluating the detection result formed by the detection, evaluating according to the cause type of the abnormality in the detection result to obtain an evaluation result and a corrective action, and transmitting the evaluation result and the corrective action to the corresponding terminal:
if the reason category relates to data abnormity, evaluating the grade of the reason category as A grade;
if the reason type relates to the abnormal voucher information of the data, evaluating the grade of the data as B grade;
if the reason category relates to detection of flow abnormality, the grade of the reason category is evaluated as grade C;
and giving a corresponding corrective action according to the evaluation level.
Specifically, class A > class B > class C, and higher levels correspond to more severe penalties in corrective actions.
Specifically, when the detected data is abnormal, the embodiment of the invention evaluates the reason and the category of the abnormal data, obtains the corrective measures through different risk levels, and transmits the evaluation result and the corrective measures to the corresponding terminal, thereby ensuring the authenticity of the terminal data and improving the safety of the terminal data.
Specifically, referring to fig. 2, an embodiment of the present invention further provides a data detection system based on big data behavior analysis, where the system includes:
agenerating module 210, configured to generate different types of detection lists according to a terminal data detection requirement, where the detection lists include detection data names and detection periods;
theallocation module 220 is configured to allocate a preset number of first-level detection personnel and second-level detection personnel according to the duration of the detection period, where the first-level detection personnel is used for performing initial detection on detection data, and the second-level detection personnel is used for rechecking the detection data;
the invokingmodule 230 is configured to invoke to-be-detected data of each terminal in a detection period according to the detection data name to obtain a first detection data list, where the to-be-detected data includes a data amount;
thesorting module 240 is configured to sort the called data to be detected according to the data amount of the data to be detected, so as to obtain a second detection data list;
and the second detection data list is used as the detection sequence of the primary detection personnel and the secondary detection personnel for performing initial detection and rechecking.
Specifically, taking a bank data audit meter as an example, a generating module generates audit lists of all departments according to audit requirements of decisions in a bank, a distributing module distributes preset number of first-level auditors and second-level auditors for audit objects according to the duration of a preset audit period in the audit lists to carry out audit twice, after the auditors are distributed, a calling module calls corresponding data to be audited from a head office, branches and branches according to names of the audit objects, because the called data to be audited is large in quantity, a sorting module sorts the called data to be audited according to the amount of the data to be audited, the larger the amount of the data to be audited is, the higher the priority of the data to be audited is, and then the auditors carry out audit twice according to the audit sequence.
Specifically, according to the embodiment of the invention, the distribution module distributes a preset number of first-level detection personnel and second-level detection personnel to the detection data in the detection list generated by the generation module according to the duration of the detection period, and then the calling module calls the data to be detected of each terminal according to the name of the detection data.
Specifically, as shown in fig. 2, the data detection system based on big data behavior analysis according to the embodiment of the present invention further includes adetection module 250 and anevaluation module 260, where the detection module is configured to perform initial detection and recheck on data to be detected in the second detection data list in sequence to obtain a detection result, and the evaluation module is configured to evaluate a detection result formed by detection to obtain an evaluation result and a corrective measure.
Specifically, in the embodiment of the present invention, the detection module performs initial detection and rechecking on the to-be-detected data in the second detection data list in sequence to obtain the detection result, and transmits the detection result to the corresponding terminal to implement effective intercommunication of the data, and the evaluation module evaluates the abnormal detection data to obtain the evaluation result and transmits the correction measure to the corresponding terminal to enable the evaluation module to perform the corresponding measure, thereby effectively improving the quality of the data.
Specifically, taking a bank data audit as an example, the bank off-site audit system based on big data behavior analysis comprises a head office terminal, a plurality of branch terminals and a plurality of branch terminals, wherein the head office terminal, the branch terminals and the branch terminals are in communication connection with the head office terminal, when data to be audited are called, the head office terminal, the branch terminals and the branch terminals call the data to be audited, and after auditing is finished, audit results, risk assessment results and corrective measures are transmitted to the head office terminal, the branch terminals and the branch terminals.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can be within the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A data detection method based on big data behavior analysis is characterized by comprising the following steps:
generating different types of detection lists according to the detection requirements of the terminal data, wherein the detection lists comprise detection data names and detection periods;
distributing a preset number of first-level detection personnel and second-level detection personnel according to the duration of the detection period, wherein the first-level detection personnel are used for carrying out initial detection on detection data, and the second-level detection personnel are used for rechecking the detection data;
calling data to be detected of each terminal in a detection period according to the detection data name to obtain a first detection data list, wherein the data to be detected comprises data volume;
the called data to be detected are sorted according to the data quantity of the data to be detected, and a second detection data list is obtained;
and the second detection data list is used as a detection sequence for the primary detection personnel and the secondary detection personnel to carry out initial detection and recheck.
2. The big data behavior analysis-based data detection method according to claim 1, wherein, when detecting staff are allocated, the duration of the detection period is divided equally, and a preset number of first-level detecting staff and second-level detecting staff are allocated to each time period, the number of the divided time periods is determined by the duration of the detection period, wherein a first period and a second period are provided, the detection period is compared with the first period and the second period, and when the duration of the detection period is greater than or equal to the second period, the duration of the detection period is divided into 3 time periods; when the time length of the detection period is less than or equal to the first period and less than the second period, dividing the time length of the detection period into 2 time periods; and when the duration of the detection period is less than the first period, dividing the duration of the detection period into 1 time period.
3. The data detection method based on big data behavior analysis according to claim 2, wherein when the data to be detected is called, the relevant data to be detected in the detection period is called from each terminal according to the name of the detection data, if the name of the detection data is a, the data of each terminal is associated with the name of the detection data a, the obtained associated data is subjected to time screening, the data in the detection period is screened out, and sorting is performed according to time, so that the first detection data list is obtained.
4. The data detection method based on big data behavior analysis according to claim 3, wherein when data to be detected is sorted, data to be detected in the first detection data list is prioritized, the data to be detected includes a data volume and a terminal name, if the terminal names corresponding to the data to be detected are different, the data to be detected in the first detection data list is prioritized according to the data volume, the higher the data volume is, the higher the priority ranking of the corresponding data to be detected is, if the data to be detected in the first detection data list is the data volume A1 and the corresponding terminal C1, the data volume A2 and the corresponding terminal C2,
when the data volume A1 is greater than the data volume A2, the priority of the data to be detected of the terminal C1 is ranked before the priority of the data to be detected of the terminal C2;
when the data volume A1= the data volume A2, the priority of the data to be detected of the terminal C1 and the priority of the data to be detected of the terminal C1 are sorted in the original order;
and when the data volume A1 is less than the data volume A2, the priority of the data to be detected of the terminal C2 is ranked behind the priority of the data to be detected of the terminal C1.
5. The data detection method based on big data behavior analysis according to claim 4, wherein when data to be detected are sorted, if the names of the terminals corresponding to the data to be detected are the same, the data volume of each terminal is calculated and the priority sorting order of the data to be detected is determined, if three data to be detected belong to the terminal C1, the data volumes of the data to be detected are A1, A2 and A3, respectively, two data to be detected belong to the terminal C2, the data volumes of the data to be detected are A4 and A5, respectively,
calculating the sum of the data volume of the terminal C1 and the data volume of the terminal C2 to obtain that the sum of the data volume of the terminal C1 is A1+ A2+ A3= E1; the sum of the data amount of the terminal C2 is A4+ A5= E2;
comparing the data volume of the terminal C1 with the data volume E1 of the terminal C2 with the data volume E2 of the terminal C2, and when the data volume E1 is larger than the data volume E2, ranking the priority of the data to be detected belonging to the terminal C1 before the priority of the data to be detected belonging to the terminal C2;
when the data volume sum E1= the data volume sum E2, the priority of the data to be detected belonging to the terminal C1 and the priority of the data to be detected belonging to the terminal C2 are sorted in the original order;
when the data amount sum E1 is less than the data amount sum E2, the priority of the data to be detected belonging to the terminal C2 is ranked before the priority of the data to be detected belonging to the terminal C1.
6. The big data behavior analysis-based data detection method according to claim 5, further comprising: and performing initial detection and rechecking on the data to be detected in the second detection data list in sequence to obtain a detection result, wherein the detection result is whether the data to be detected is abnormal or not, if so, the reason of the abnormality is obtained, and the detection result is transmitted to the corresponding terminal.
7. The big data behavior analysis based data detection method according to claim 6, further comprising: evaluating a detection result formed by detection, evaluating according to the reason type of abnormality in the detection result to obtain an evaluation result and a correction measure, and transmitting the evaluation result and the correction measure to a corresponding terminal;
if the reason category relates to data abnormity, evaluating the grade of the reason category as A grade;
if the reason type relates to the abnormal voucher information of the data, evaluating the grade of the data as B grade;
if the reason category relates to detection of flow abnormality, evaluating the level as C level;
wherein, the grade A is more than the grade B is more than the grade C, and corresponding corrective measures are given according to the evaluation grade.
8. A big data behavior analysis-based data detection system applying the big data behavior analysis-based data detection method according to any one of claims 1 to 7, comprising:
the generating module is used for generating different types of detection lists according to the detection requirements of the terminal data, and the detection lists comprise detection data names and detection periods;
the distribution module is used for distributing a preset number of first-level detection personnel and second-level detection personnel according to the duration of the detection period, wherein the first-level detection personnel is used for carrying out initial detection on detection data, and the second-level detection personnel is used for rechecking the detection data;
the calling module is used for calling to-be-detected data of each terminal in a detection period according to the detection data name to obtain a first detection data list, wherein the to-be-detected data comprises data volume;
the sorting module is used for sorting the called data to be detected according to the data quantity of the data to be detected to obtain a second detection data list;
and the second detection data list is used as the detection sequence of the primary detection personnel and the secondary detection personnel for performing initial detection and rechecking.
9. The big data behavior analysis based data detection system according to claim 8, further comprising a detection module and an evaluation module, wherein the detection module is configured to perform initial detection and recheck on the data to be detected in the second detection data list in sequence to obtain a detection result, and the evaluation module is configured to evaluate the detection result formed by the detection to obtain an evaluation result and a corrective measure.
10. The big data behavior analysis based data detection system according to claim 9, wherein the detection module performs a time period average division on the duration of the detection period when allocating the detection personnel, allocates a preset number of primary detection personnel and secondary detection personnel to each time period, and the number of the divided time periods is determined by the duration of the detection period, wherein a first period and a second period are provided, compares the detection period with the first period and the second period, and divides the duration of the detection period into 3 time periods when the duration of the detection period is greater than or equal to the second period; when the time length of the detection period is less than or equal to the first period and less than the second period, dividing the time length of the detection period into 2 time periods; and when the duration of the detection period is less than the first period, dividing the duration of the detection period into 1 time segment.
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