CN110532301A - Auditing method, system and readable storage medium storing program for executing - Google Patents
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Abstract
The present invention provides a kind of auditing method, system and readable storage medium storing program for executing, which comprises obtains Audit data；The Audit data is analyzed with preset audit model, determines auditing result；The auditing result, including it is following at least one: audit aim point and auditing errors point；The audit model determines the audit aim point in the Audit data for analyzing the Audit data；And error detection is carried out to the Audit data, be audited erroneous point.Audit processing directly is carried out to Audit data by the audit model constructed in advance, is avoided and is manually calculated, improves work efficiency and accuracy rate.
Description
Technical field
The present invention relates to technical field of data processing more particularly to a kind of auditing methods, system and readable storage medium storing program for executing.
Background technique
In recent years, with the rapid development of computer technology and informatization, the information-based range of Economic Management Activities
It also grows with each passing hour with depth, the audit for supervising as economic activity, evaluating and discerning encounters unprecedented challenge, tradition
Manual audit can not adapt to the audit demand under Information Condition, audit may will lead to error by hand, and efficiency also compares
It is lower.The development of the informationization and audit itself of the audit target requires that audit operation mode must grow with each passing hour, and makes corresponding
Adjustment.
Therefore, responsive message development trend updates audit and supervision theory, creates more efficient, more accurate auditing method
It is the faced new issue of the consistently online audit of current informationization.
Summary of the invention
In order to solve at least one above-mentioned technical problem, the invention proposes a kind of auditing method, system and readable storages
Medium.
To achieve the goals above, first aspect present invention proposes a kind of auditing method, which comprises obtains and examines
It counts；
The Audit data is analyzed with preset audit model, determines auditing result；The auditing result, packet
Include it is following at least one: audit aim point and auditing errors point；
The audit model determines the audit aim point in the Audit data for analyzing the Audit data；With
And error detection is carried out to the Audit data, be audited erroneous point.
Specifically, the acquisition Audit data, comprises at least one of the following:
Data acquisition instructions are sent at least one database, receive the Audit data of at least one database transmission；
Access instruction is sent at least one database, after the acceptance message for receiving the transmission of at least one database, is used
WebService, Http method of servicing access at least one described database to obtain the Audit data；
Data acquisition instructions are sent to central database, receive the Audit data that the central database is sent；Wherein, institute
Central database is stated, obtains the Audit data from least one described database to periodicity；
Access instruction is sent to the central database, after receiving the acceptance message that central database is sent, is used
WebService, Http method of servicing access the central database to obtain the Audit data.
Specifically, it is described the Audit data is analyzed with preset audit model before, the method is also wrapped
It includes: pre-processing the Audit data by preset rules；
It is described to pre-process the Audit data by preset rules, comprising:
Analyze the Audit data with semantic analysis, determine the data type for including in the Audit data and
The corresponding numerical value of each data type；
Judge whether the data type belongs to preset required data type, it is preset to determine that the data type belongs to
When required data type, the data type and its corresponding numerical value are extracted；
Judge whether the numerical value meets preset value type, determines that the numerical value meets preset value type, then
Retain the numerical value；It determines that the numerical value does not meet preset value type, then type conversion is carried out to the numerical value, so that institute
Numerical value is stated to save by preset value type is corresponding.
Specifically, the method also includes: generate the audit model；It is described to generate the audit model, comprising:
Obtain preset neural network structure；The neural network structure includes at least one input node and at least two
Output node；
Obtain audit of sample data and its corresponding trained label；The trained label include: audit of sample target point and
Audit of sample erroneous point；
With the audit of sample data and its corresponding trained label training neural network structure, after being trained
Neural network structure, as the audit model.
Specifically, the data type of the Audit data, comprising: financial domain business data and trans-sectoral business numeric field data；
It is described finance domain business data, including it is following at least one: running cost reimbursement, reimbursing travelling expenses, engineering pay the bill,
The business datums such as electricity charge payment, emolument payment；
The trans-sectoral business numeric field data, including at least one following data: engineering project, engineering contract, goods and materials contract, engineering
It is receivable paid that preliminary budget, goods and materials go out storage bill, project final report, marketing financial counting, the electricity charge；
At least one described input node, comprising: the first input node, the second input node and third input node；
It is described to use the audit of sample data and its corresponding trained label training neural network structure, comprising:
The trans-sectoral business numeric field data of sample finance domain business data and sample is inputted into the first input node；
Sample finance domain business data is inputted into the second input node；
The trans-sectoral business numeric field data of sample is inputted into third input node；
Received data and described right are distinguished according to first input node, the second input node and third input node
The training label training neural network structure answered.
Specifically, after the Audit data by preset rules pretreatment, the method also includes:
The numerical value of N of target Audit data is determined as the following formula；
Wherein, R indicates that overall value, BV indicate that risk factor, TM indicate that tolerable reports an error, and E indicates that expection reports an error, r table
Show that flare factor, v indicate data importance；
N number of target Audit data is selected from the pretreated Audit data；
Correspondingly, described analyze the Audit data with preset audit model, comprising:
N number of target Audit data is analyzed with preset audit model.
Second aspect of the present invention also proposes a kind of auditing system, and the auditing system includes: memory and processor, described
Include a kind of auditing method program in memory, the auditing method program realizes following steps when being executed by the processor:
Obtain Audit data；
The Audit data is analyzed with preset audit model, determines auditing result；The auditing result, packet
Include it is following at least one: audit aim point and auditing errors point；
The audit model determines the audit aim point in the Audit data for analyzing the Audit data；With
And error detection is carried out to the Audit data, be audited erroneous point.
Specifically, the acquisition Audit data, comprises at least one of the following:
Data acquisition instructions are sent at least one database, receive the Audit data of at least one database transmission；
Access instruction is sent at least one database, after the acceptance message for receiving the transmission of at least one database, is used
WebService, Http method of servicing access at least one described database to obtain the Audit data；
Data acquisition instructions are sent to central database, receive the Audit data that the central database is sent；Wherein, institute
Central database is stated, obtains the Audit data from least one described database to periodicity；
Access instruction is sent to the central database, after receiving the acceptance message that central database is sent, is used
WebService, Http method of servicing access the central database to obtain the Audit data.
Specifically, it is described the Audit data is analyzed with preset audit model before, the method is also wrapped
It includes: pre-processing the Audit data by preset rules；
It is described to pre-process the Audit data by preset rules, comprising:
Analyze the Audit data with semantic analysis, determine the data type for including in the Audit data and
The corresponding numerical value of each data type；
Judge whether the data type belongs to preset required data type, it is preset to determine that the data type belongs to
When required data type, the data type and its corresponding numerical value are extracted；
Judge whether the numerical value meets preset value type, determines that the numerical value meets preset value type, then
Retain the numerical value；It determines that the numerical value does not meet preset value type, then type conversion is carried out to the numerical value, so that institute
Numerical value is stated to save by preset value type is corresponding.
Third aspect present invention also proposes a kind of computer readable storage medium, wraps in the computer readable storage medium
A kind of auditing method program is included, when the auditing method program is executed by processor, is realized such as a kind of above-mentioned auditing method
Step.
The embodiment of the invention provides a kind of auditing method, system and storage mediums, obtain Audit data；With preset
Audit model analyzes the Audit data, determines auditing result；The auditing result, including it is following at least one: examine
Count target point and auditing errors point；The audit model determines examining in the Audit data for analyzing the Audit data
Count target point；And error detection is carried out to the Audit data, be audited erroneous point.In the present solution, by constructing in advance
Audit model audit processing directly is carried out to Audit data, avoid and manually calculate, improve work efficiency and accuracy rate.
Additional aspect and advantage of the invention will provide in following description section, will partially become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of auditing method provided in an embodiment of the present invention；
Fig. 2 is a kind of structural schematic diagram of audit device provided in an embodiment of the present invention；
Fig. 3 is a kind of structural schematic diagram of auditing system provided in an embodiment of the present invention.
Specific embodiment
To better understand the objects, features and advantages of the present invention, with reference to the accompanying drawing and specific real
Applying mode, the present invention is further described in detail.It should be noted that in the absence of conflict, the implementation of the application
Feature in example and embodiment can be combined with each other.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, still, the present invention may be used also
To be implemented using other than the one described here other modes, therefore, protection scope of the present invention is not by described below
Specific embodiment limitation.
Fig. 1 is a kind of flow diagram of auditing method provided in an embodiment of the present invention；As shown in Figure 1, the method can
To be applied to load by intelligent electronic devices such as server, the computers of auditing system；The described method includes:
Step 101 obtains Audit data.
Here, the Audit data, comprising: financial domain business data and trans-sectoral business numeric field data.
Wherein, the financial domain business data, including it is following at least one: running cost reimbursement, reimbursing travelling expenses, engineering
The business datums such as payment, electricity charge payment, emolument payment；
The trans-sectoral business numeric field data, including at least one following data: engineering project, engineering contract, goods and materials contract, engineering
It is receivable paid that preliminary budget, goods and materials go out storage bill, project final report, marketing financial counting, the electricity charge.
Specifically, the acquisition Audit data, comprises at least one of the following:
Data acquisition instructions are sent at least one database, receive the Audit data of at least one database transmission；
Access instruction is sent at least one database, after the acceptance message for receiving the transmission of at least one database, is used
WebService, Http method of servicing access at least one described database to obtain the Audit data；
Data acquisition instructions are sent to central database, receive the Audit data that the central database is sent；Wherein, institute
Central database is stated, obtains the Audit data from least one described database to periodicity；
Access instruction is sent to the central database, after receiving the acceptance message that central database is sent, is used
WebService, Http method of servicing access the central database to obtain the Audit data.
Step 102 analyzes the Audit data with preset audit model, determines auditing result；It is described to examine
Meter as a result, include it is following at least one: audit aim point and auditing errors point.
Here, the audit model determines the audit aim in the Audit data for analyzing the Audit data
Point；And error detection is carried out to the Audit data, be audited erroneous point.
There are problems for integrality, consistency, normalization for guarantee Audit data etc., before carrying out data processing
Need to carry out data unification.
Specifically, it is described the Audit data is analyzed with preset audit model before, the method is also wrapped
It includes: pre-processing the Audit data by preset rules；
It is described to pre-process the Audit data by preset rules, comprising:
Analyze the Audit data with semantic analysis, determine the data type for including in the Audit data and
The corresponding numerical value of each data type；
Judge whether the data type belongs to preset required data type, it is preset to determine that the data type belongs to
When required data type, the data type and its corresponding numerical value are extracted；
Judge whether the numerical value meets preset value type, determines that the numerical value meets preset value type, then
Retain the numerical value；It determines that the numerical value does not meet preset value type, then type conversion is carried out to the numerical value, so that institute
Numerical value is stated to save by preset value type is corresponding.
Here, the preset value type can be preset and be saved by developer.
The value type may include: Integer (integer), Long (long), Single (single-precision floating point
Type), Double (double-precision floating point type) and Currency (currency)；It, can be with by pretreatment to be convenient for data processing
The value type of each data is subjected to unification in advance, in order to calculate, improves audit efficiency.
Specifically, the method also includes: generate the audit model；The audit model, it then follows the mesh of audit operations
It marks and creates, the value of audit model is that identification risk can be reached, disclosures risks, the audit work target of prevention and control risk,
Core technology is from audit demand, according to mathematical logic and service route, in conjunction with the Audit data of acquisition, building audit mould
Type.Big data analysis is realized under model calculating, is helped audit group determining audit emphasis and a doubtful point, is finally realized audit aim.
It is specifically, described to generate the audit model, comprising:
Obtain preset neural network structure；The neural network structure includes at least one input node and at least two
Output node；
Obtain audit of sample data and its corresponding trained label；The trained label include: audit of sample target point and
Audit of sample erroneous point；
With the audit of sample data and its corresponding trained label training neural network structure, after being trained
Neural network structure, as the audit model.
The audit of sample data and its corresponding trained label are by developer according to historical auditing data and its audit
As a result it obtains.The audit of sample data and its corresponding trained label are thought into one group of audit of sample data pair.
The audit of sample data and its quantity of corresponding trained label is at least one, i.e. audit of sample data pair
Quantity is at least one.At least one audit of sample data is collected to training set and detection is divided into；By in the training set
Audit of sample data the training neural network structure, by detection collection to the neural network structure, until finally obtaining
Obtain the audit model.
It should be noted that carry out combing, analysis to audit operations demand before generating audit model, clearly examine
The contents such as the index in the field, monitoring supervised, the requirement of management are counted to have to provide foundation for work such as modelling, tests
Body, which refers to, determines required Audit data and audit aim (audit aim can be used as trained label).
Specifically, the data type of the Audit data, including following two major classes: financial domain business data and across business domains
Data；
At least one described input node, comprising: the first input node, the second input node and third input node；
It is described to use the audit of sample data and its corresponding trained label training neural network structure, comprising:
The trans-sectoral business numeric field data of sample finance domain business data and sample is inputted into the first input node；
Sample finance domain business data is inputted into the second input node；
The trans-sectoral business numeric field data of sample is inputted into third input node；
Received data and described right are distinguished according to first input node, the second input node and third input node
The training label training neural network structure answered.
Here, it is contemplated that succinct generalization, ambiguity, the relevance of above-mentioned items Audit data, i.e., from respective angle,
It is trained again from the angle that is mutually related.So as to excavate existing data movement rule and data relationship mode, thus
Audit trail is found whereby, and then is verified and confirmed to obtain relevant audit evidence.
Specifically, after the determining auditing result, the method also includes:
After determining the auditing errors point, corresponding error and solution scheme is provided based on the auditing errors point；It will be described
Error and solution scheme is sent to user terminal.
Here, various auditing errors points and corresponding solution rule of thumb can be set and be protected in advance by developer
In presence server, when determining has auditing errors point, corresponding error and solution scheme is directly provided to user, so that user is fast
It is fast to solve the problems, such as, it improves efficiency.
In the present embodiment, to improve treatment effeciency, selected section data processing.Specifically, institute is pre-processed by preset rules
After stating Audit data, the method also includes:
The numerical value of N of target Audit data is determined as the following formula；
Wherein, R indicates that overall value, BV indicate that risk factor, TM indicate that tolerable reports an error, and E indicates that expection reports an error, r table
Show that flare factor, v indicate data importance；
N number of target Audit data is selected from the pretreated Audit data；
Correspondingly, described analyze the Audit data with preset audit model, comprising:
N number of target Audit data is analyzed with preset audit model.
Wherein, BV, TM, E, r preset and save；It can be different for different types of Audit data TM, E and BV；
Wherein, the overall value carries out cumulative calculation according to the Audit data and determines.The value of the v can be 0.5 to 1.5
Between any number, specific value according to the type of Audit data determine, i.e., for above-mentioned running cost reimbursement, travel charge report
Business datums, engineering project, engineering contract, goods and materials contract, the engineerings such as pin, engineering payment, electricity charge payment, emolument payment are generally pre-
Calculate, goods and materials go out storage bill, project final report, marketing financial counting, the electricity charge are receivable paid, different values can be taken.Audit number
According to more important, the specific value of v is higher, and the numerical value of required target Audit data is more, and accuracy is higher.V specifically can be by
User determines according to self-demand.
Fig. 2 is a kind of structural schematic diagram of audit device provided in an embodiment of the present invention；As shown in Fig. 2, the audit dress
It sets, may include: first processing module and Second processing module.
Wherein, the first processing module, for obtaining Audit data；
The Second processing module is determined and is examined for analyzing with preset audit model the Audit data
Count result；The auditing result, including it is following at least one: audit aim point and auditing errors point；Here, the audit mould
Type determines the audit aim point in the Audit data for analyzing the Audit data；And to the Audit data into
Row error detection, be audited erroneous point.
It should be noted that above-mentioned audit device belongs to unification when being audited, with the auditing method shown in FIG. 1
Design, therefore repeat no more.
Fig. 3 is a kind of structural schematic diagram of auditing system provided in an embodiment of the present invention.As shown in figure 3, the present invention second
Aspect also proposes that a kind of auditing system 3, the auditing system 3 include: memory 31 and processor 32, wraps in the memory 31
A kind of auditing method program is included, the auditing method program realizes following steps when being executed by the processor 32:
Obtain Audit data；
The Audit data is analyzed with preset audit model, determines auditing result；The auditing result, packet
Include it is following at least one: audit aim point and auditing errors point；
The audit model determines the audit aim point in the Audit data for analyzing the Audit data；With
And error detection is carried out to the Audit data, be audited erroneous point.
It should be noted that system of the invention can be operated in the terminal devices such as PC, mobile phone, PAD.
It should be noted that the processor can be central processing unit (Central Processing Unit,
CPU), it can also be other general processors, Digital Signal Processing (Digital Signal Processor, DSP), dedicated collection
At circuit (Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
Further, Audit data is obtained, is comprised at least one of the following:
Data acquisition instructions are sent at least one database, receive the Audit data of at least one database transmission；
Access instruction is sent at least one database, after the acceptance message for receiving the transmission of at least one database, is used
WebService, Http method of servicing access at least one described database to obtain the Audit data；
Data acquisition instructions are sent to central database, receive the Audit data that the central database is sent；Wherein, institute
Central database is stated, obtains the Audit data from least one described database to periodicity；
Access instruction is sent to the central database, after receiving the acceptance message that central database is sent, is used
WebService, Http method of servicing access the central database to obtain the Audit data.
Further, before analyzing with preset audit model the Audit data, the method also includes:
The Audit data is pre-processed by preset rules；
It is described to pre-process the Audit data by preset rules, comprising:
Analyze the Audit data with semantic analysis, determine the data type for including in the Audit data and
The corresponding numerical value of each data type；
Judge whether the data type belongs to preset required data type, it is preset to determine that the data type belongs to
When required data type, the data type and its corresponding numerical value are extracted；
Judge whether the numerical value meets preset value type, determines that the numerical value meets preset value type, then
Retain the numerical value；It determines that the numerical value does not meet preset value type, then type conversion is carried out to the numerical value, so that institute
Numerical value is stated to save by preset value type is corresponding.
Further, the method also includes: generate the audit model；It is described to generate the audit model, comprising:
Obtain preset neural network structure；The neural network structure includes at least one input node and at least two
Output node；
Obtain audit of sample data and its corresponding trained label；The trained label include: audit of sample target point and
Audit of sample erroneous point；
With the audit of sample data and its corresponding trained label training neural network structure, after being trained
Neural network structure, as the audit model.
Further, the data type of the Audit data, comprising: financial domain business data and trans-sectoral business numeric field data；
It is described finance domain business data, including it is following at least one: running cost reimbursement, reimbursing travelling expenses, engineering pay the bill,
The business datums such as electricity charge payment, emolument payment；
The trans-sectoral business numeric field data, including at least one following data: engineering project, engineering contract, goods and materials contract, engineering
It is receivable paid that preliminary budget, goods and materials go out storage bill, project final report, marketing financial counting, the electricity charge；
At least one described input node, comprising: the first input node, the second input node and third input node；
It is described to use the audit of sample data and its corresponding trained label training neural network structure, comprising:
The trans-sectoral business numeric field data of sample finance domain business data and sample is inputted into the first input node；
Sample finance domain business data is inputted into the second input node；
The trans-sectoral business numeric field data of sample is inputted into third input node；
Received data and described right are distinguished according to first input node, the second input node and third input node
The training label training neural network structure answered.
Further, after the determining auditing result, the method also includes:
After determining the auditing errors point, corresponding error and solution scheme is provided based on the auditing errors point；It will be described
Error and solution scheme is sent to user terminal.
Further, after the Audit data by preset rules pretreatment, the method also includes:
The numerical value of N of target Audit data is determined as the following formula；
Wherein, R indicates that overall value, BV indicate that risk factor, TM indicate that tolerable reports an error, and E indicates that expection reports an error, r table
Show that flare factor, v indicate data importance；
N number of target Audit data is selected from the pretreated Audit data；
Correspondingly, described analyze the Audit data with preset audit model, comprising:
N number of target Audit data is analyzed with preset audit model.
Third aspect present invention also proposes a kind of computer readable storage medium, wraps in the computer readable storage medium
A kind of auditing method program is included, when the auditing method program is executed by processor, is realized such as a kind of above-mentioned auditing method
Step.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it
Its mode is realized.Apparatus embodiments described above are merely indicative, for example, the division of the unit, only
A kind of logical function partition, there may be another division manner in actual implementation, such as: multiple units or components can combine, or
It is desirably integrated into another system, or some features can be ignored or not executed.In addition, shown or discussed each composition portion
Mutual coupling or direct-coupling or communication connection is divided to can be through some interfaces, the INDIRECT COUPLING of equipment or unit
Or communication connection, it can be electrical, mechanical or other forms.
Above-mentioned unit as illustrated by the separation member, which can be or may not be, to be physically separated, aobvious as unit
The component shown can be or may not be physical unit；Both it can be located in one place, and may be distributed over multiple network lists
In member；Some or all of units can be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
In addition, each functional unit in various embodiments of the present invention can be fully integrated in one processing unit, it can also
To be each unit individually as a unit, can also be integrated in one unit with two or more units；It is above-mentioned
Integrated unit both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can store in computer-readable storage medium, which exists
When execution, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes: movable storage device, read-only deposits
Reservoir (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or
The various media that can store program code such as CD.
If alternatively, the above-mentioned integrated unit of the present invention is realized in the form of software function module and as independent product
When selling or using, it also can store in a computer readable storage medium.Based on this understanding, the present invention is implemented
Substantially the part that contributes to existing technology can be embodied in the form of software products the technical solution of example in other words,
The computer software product is stored in a storage medium, including some instructions are used so that computer equipment (can be with
It is personal computer, server or network equipment etc.) execute all or part of each embodiment the method for the present invention.
And storage medium above-mentioned includes: that movable storage device, ROM, RAM, magnetic or disk etc. are various can store program code
Medium.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
Claims (10)
1. a kind of auditing method, which is characterized in that the described method includes:
Obtain Audit data；
The Audit data is analyzed with preset audit model, determines auditing result；The auditing result, including with
Descend at least one: audit aim point and auditing errors point；The audit model, for analyzing the Audit data, determine described in
Audit aim point in Audit data；And error detection is carried out to the Audit data, be audited erroneous point.
2. a kind of auditing method according to claim 1, which is characterized in that the acquisition Audit data, including with down toward
Few one kind:
Data acquisition instructions are sent at least one database, receive the Audit data of at least one database transmission；
Access instruction is sent at least one database, after the acceptance message for receiving the transmission of at least one database, is used
WebService, Http method of servicing access at least one described database to obtain the Audit data；
Data acquisition instructions are sent to central database, receive the Audit data that the central database is sent；Wherein, in described
Heart database obtains the Audit data from least one described database to periodicity；
Access instruction is sent to the central database, after receiving the acceptance message that central database is sent, is used
WebService, Http method of servicing access the central database to obtain the Audit data.
3. a kind of auditing method according to claim 1, which is characterized in that described to use preset audit model to described
Before Audit data is analyzed, the method also includes: the Audit data is pre-processed by preset rules；
It is described to pre-process the Audit data by preset rules, comprising:
The Audit data is analyzed with semantic analysis, determines the data type for including in the Audit data and each number
According to the corresponding numerical value of type；
Judge whether the data type belongs to preset required data type, it is preset required to determine that the data type belongs to
When data type, the data type and its corresponding numerical value are extracted；
Judge whether the numerical value meets preset value type, determines that the numerical value meets preset value type, then retain
The numerical value；It determines that the numerical value does not meet preset value type, then type conversion is carried out to the numerical value, so that the number
Value is saved by preset value type is corresponding.
4. a kind of auditing method according to claim 1, which is characterized in that the method also includes: generate the audit
Model；It is described to generate the audit model, comprising:
Obtain preset neural network structure；The neural network structure includes at least one input node and at least two outputs
Node；
Obtain audit of sample data and its corresponding trained label；The trained label includes: audit of sample target point and sample
Auditing errors point；
Mind with the audit of sample data and its corresponding trained label training neural network structure, after being trained
Through network structure, as the audit model.
5. a kind of auditing method according to claim 1, which is characterized in that the data type of the Audit data, comprising:
Financial domain business data and trans-sectoral business numeric field data；
It is described finance domain business data, including it is following at least one: running cost reimbursement, reimbursing travelling expenses, engineering pay the bill, the electricity charge
The business datums such as payment, emolument payment；
The trans-sectoral business numeric field data, including at least one following data: engineering project, engineering contract, goods and materials contract, engineering are generally pre-
It calculates, goods and materials go out storage bill, project final report, marketing financial counting, the electricity charge are receivable paid；
At least one described input node, comprising: the first input node, the second input node and third input node；
It is described to use the audit of sample data and its corresponding trained label training neural network structure, comprising:
The trans-sectoral business numeric field data of sample finance domain business data and sample is inputted into the first input node；
Sample finance domain business data is inputted into the second input node；
The trans-sectoral business numeric field data of sample is inputted into third input node；
Received data and described corresponding are distinguished according to first input node, the second input node and third input node
The training label training neural network structure.
6. a kind of auditing method according to claim 3, which is characterized in that described to pre-process the audit by preset rules
After data, the method also includes:
The numerical value of N of target Audit data is determined as the following formula；
Wherein, R indicates that overall value, BV indicate that risk factor, TM indicate that tolerable reports an error, and E indicates that expection reports an error, and r indicates to expand
Coefficient is opened, v indicates data importance；
N number of target Audit data is selected from the pretreated Audit data；
Correspondingly, described analyze the Audit data with preset audit model, comprising:
N number of target Audit data is analyzed with preset audit model.
7. a kind of auditing system, which is characterized in that the auditing system includes: memory and processor, is wrapped in the memory
A kind of auditing method program is included, the auditing method program realizes following steps when being executed by the processor: obtaining audit number
According to；
The Audit data is analyzed with preset audit model, determines auditing result；The auditing result, including with
Descend at least one: audit aim point and auditing errors point；
The audit model determines the audit aim point in the Audit data for analyzing the Audit data；And it is right
The Audit data carries out error detection, and be audited erroneous point.
8. a kind of auditing system according to claim 7, which is characterized in that the acquisition Audit data, including with down toward
Few one kind:
Data acquisition instructions are sent at least one database, receive the Audit data of at least one database transmission；
Access instruction is sent at least one database, after the acceptance message for receiving the transmission of at least one database, is used
WebService, Http method of servicing access at least one described database to obtain the Audit data；
Data acquisition instructions are sent to central database, receive the Audit data that the central database is sent；Wherein, in described
Heart database obtains the Audit data from least one described database to periodicity；
Access instruction is sent to the central database, after receiving the acceptance message that central database is sent, is used
WebService, Http method of servicing access the central database to obtain the Audit data.
9. a kind of auditing system according to claim 7, which is characterized in that described to use preset audit model to described
Before Audit data is analyzed, the method also includes: the Audit data is pre-processed by preset rules；
It is described to pre-process the Audit data by preset rules, comprising:
The Audit data is analyzed with semantic analysis, determines the data type for including in the Audit data and each number
According to the corresponding numerical value of type；
Judge whether the data type belongs to preset required data type, it is preset required to determine that the data type belongs to
When data type, the data type and its corresponding numerical value are extracted；
Judge whether the numerical value meets preset value type, determines that the numerical value meets preset value type, then retain
The numerical value；It determines that the numerical value does not meet preset value type, then type conversion is carried out to the numerical value, so that the number
Value is saved by preset value type is corresponding.
10. a kind of computer readable storage medium, which is characterized in that include a kind of audit in the computer readable storage medium
Method program when the auditing method program is executed by processor, realizes such as one kind described in any one of claims 1 to 6
The step of auditing method.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111177779A (en) * | 2019-12-24 | 2020-05-19 | 深圳昂楷科技有限公司 | Database auditing method, device thereof, electronic equipment and computer storage medium |
| CN112465397A (en) * | 2020-12-15 | 2021-03-09 | 广东电网有限责任公司 | Audit data analysis method and device |
| CN112651618A (en) * | 2020-12-21 | 2021-04-13 | 国家电网有限公司大数据中心 | Construction method of audit dimension model for online audit of metering data |
| CN112711772A (en) * | 2020-12-30 | 2021-04-27 | 杭州未名信科科技有限公司 | Auditing system, method and storage medium for function execution in service |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5845285A (en) * | 1997-01-07 | 1998-12-01 | Klein; Laurence C. | Computer system and method of data analysis |
| CN106503102A (en) * | 2016-10-17 | 2017-03-15 | 汉蓝（北京）科技有限公司 | A kind of search engine formula audit analysis method |
| CN107832429A (en) * | 2017-11-14 | 2018-03-23 | 广州供电局有限公司 | audit data processing method and system |
| CN108268656A (en) * | 2018-01-31 | 2018-07-10 | 合肥和钧正策信息技术有限公司 | A kind of big data intelligent cloud auditing method and system |
| CN108460521A (en) * | 2018-02-02 | 2018-08-28 | 广州供电局有限公司 | The recommendation method and system of the audit target |
| CN108959349A (en) * | 2018-04-23 | 2018-12-07 | 厦门快商通信息技术有限公司 | A kind of financial audit circular for confirmation system |
-
2019
- 2019-08-30 CN CN201910815693.2A patent/CN110532301B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5845285A (en) * | 1997-01-07 | 1998-12-01 | Klein; Laurence C. | Computer system and method of data analysis |
| CN106503102A (en) * | 2016-10-17 | 2017-03-15 | 汉蓝（北京）科技有限公司 | A kind of search engine formula audit analysis method |
| CN107832429A (en) * | 2017-11-14 | 2018-03-23 | 广州供电局有限公司 | audit data processing method and system |
| CN108268656A (en) * | 2018-01-31 | 2018-07-10 | 合肥和钧正策信息技术有限公司 | A kind of big data intelligent cloud auditing method and system |
| CN108460521A (en) * | 2018-02-02 | 2018-08-28 | 广州供电局有限公司 | The recommendation method and system of the audit target |
| CN108959349A (en) * | 2018-04-23 | 2018-12-07 | 厦门快商通信息技术有限公司 | A kind of financial audit circular for confirmation system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111177779A (en) * | 2019-12-24 | 2020-05-19 | 深圳昂楷科技有限公司 | Database auditing method, device thereof, electronic equipment and computer storage medium |
| CN112465397A (en) * | 2020-12-15 | 2021-03-09 | 广东电网有限责任公司 | Audit data analysis method and device |
| CN112651618A (en) * | 2020-12-21 | 2021-04-13 | 国家电网有限公司大数据中心 | Construction method of audit dimension model for online audit of metering data |
| CN112711772A (en) * | 2020-12-30 | 2021-04-27 | 杭州未名信科科技有限公司 | Auditing system, method and storage medium for function execution in service |
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