CN115391399A - Method and system for collecting service system data - Google Patents
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Abstract
The invention discloses a method and a system for collecting business system data, which comprises the steps of interpreting internal data of an enterprise, extracting all business data in a target business system based on a full data extraction technology, wherein the business data comprises data of a bid and tender purchasing system, a supplier system and a financial system; performing data analysis on the service data, and acquiring the strength and accuracy of the data association relationship through data relationship evidence reasoning operation; matching the service data based on the data characteristic items, and extracting value data; and comparing the types and the values of the value data by using a characteristic item set comparison engine, and persistently storing correct information. According to the method and the device, data can be intelligently correlated, fused, deduced and calculated, the true accuracy degree of the data and the correlation weight among the data are identified, and manual retrieval and calculation of a large amount of data by the bidding audit supervisor in the audit investigation process are reduced.
Description
Technical Field
The invention relates to the field of data collection application, in particular to a method and a system for collecting service system data.
Background
Enterprise auditing is independent economic supervision activities of examining the authenticity, legality and profitability of the finance and finance of an enterprise and related economic activities by professional institutions and personnel according to laws so as to maintain the financial institution discipline, improve the operation management of audited units, improve the economic benefits of audited units and the like. In the prior art, the service data processing is divided into manual processing and computer processing, the manual processing speed is low, auditing and supervising personnel need to manually retrieve and calculate a large amount of data in the auditing and surveying process, the existing computer processing is limited to data statistics and data extraction of the service data, and the true accuracy of the data and the correlation weight of a data piece are difficult to identify.
Disclosure of Invention
In order to solve the technical problems that the manual processing speed is slow, an audit supervisor needs to manually search and calculate a large amount of data in the audit investigation process, the existing computer processing is limited to data statistics and data extraction on business data, and the truth and accuracy of the data and the correlation weight of a data piece are difficult to identify, the invention provides a method and a system for collecting business system data, which are used for solving the technical problems,
according to an aspect of the present invention, a method for collecting service system data is provided, including:
s1: interpreting internal data of an enterprise, and extracting all business data in a target business system based on a full data extraction technology, wherein the business data comprises data of a bidding purchasing system, a supplier system and a financial system;
s2: performing data analysis on the service data, and acquiring the strength and accuracy of a data association relation through data relation evidence reasoning operation;
s3: matching the service data based on the data characteristic items, and extracting value data;
s4: and comparing the types and the values of the value data by using a characteristic item set comparison engine, and persistently storing correct information.
In some specific embodiments, the obtaining of the strength and accuracy of the data association relationship through the data relationship evidence reasoning operation in S2 specifically includes:
s21: determining sample data read from within a business system
And distribution of service intervals
Matching similarity, and obtaining the similarity sum a of all sample pairs n，j ；
S22: obtaining a single characteristic likelihood function table by calculation
Wherein, y n For each sample feature value class, δ n The number of samples;
s23: inputting characteristic likelihood function table
Obtaining a reliability matrix table;
s24: fusing evidences under evidence reasoning rules to obtain characteristics
In category y n Degree of confidence in
S25: obtaining an Attribute X i Reliability of (2)
Wherein Q is i Representation can be directly based on attribute X i The number of samples of a specific class is determined.
In some specific embodiments, the matching of the service data based on the data feature item in S3 specifically includes type matching, semantic matching, and data quality matching.
In some specific embodiments, S4 specifically includes:
s41: taking the real sample data set as a test sample, taking the obtained data as a training sample, carrying out value normalization processing on the feature item set, and normalizing the read training sample data to be in the interval of [0,1 ];
s42: establishing a neural network, setting training parameters of the training parameters to the BP neural network, and training;
s43: and reading a test sample, inputting the test sample into a neural network for testing, analyzing a result, counting the accuracy of sample identification, and generating a feature item set reference if the accuracy of sample identification is low.
In some specific embodiments, the training parameters include accuracy, number of times, and a fixed value.
According to a second aspect of the invention, a computer-readable storage medium having one or more computer programs stored thereon, wherein the one or more computer programs, when executed by a computer processor, implement the above-described method.
According to a third aspect of the present invention, a system for collecting business system data is provided, the system comprising:
a full data extraction unit: the system is configured to interpret internal data of an enterprise, and extract all business data in a target business system based on a full-scale data extraction technology, wherein the business data comprises data of a bid and tender purchasing system, a supplier system and a financial system;
a data analysis unit: the configuration is used for carrying out data analysis on the service data and obtaining the strength and the accuracy of the data association relationship through data relationship evidence reasoning operation;
a data matching unit: the data matching method is configured and used for matching service data based on the data characteristic items and extracting value data;
a data storage unit: the system is configured to compare the types and the values of the value data by using a characteristic item set comparison engine and persistently store correct information.
In some specific embodiments, the obtaining of the strength and accuracy of the data association relationship through the data relationship evidence reasoning operation in the data analysis unit specifically includes determining read sample data
And distribution of service intervals
Matching similarity, and obtaining the similarity sum a of all sample pairs n，j (ii) a Calculating to obtain single characteristic likelihood function table
Wherein, y n For each sample eigenvalue class, δ n The number of samples; inputting characteristic likelihood function table
Obtaining a reliability matrix table; fusing evidences under evidence reasoning rules to obtain characteristics
In category y n Degree of confidence in
Obtaining an Attribute X i Reliability of (2)
Wherein Q is i The representation can be directly based on the attribute X i The number of samples of a specific class is determined.
In some specific embodiments, the matching of the service data based on the data feature items in the data matching unit specifically includes type matching, semantic matching, and data quality matching.
In some specific embodiments, the comparing the type and the value of the value data in the data storage unit by using the feature item set comparison engine specifically includes: taking the real sample data set as a test sample, taking the obtained data as a training sample, carrying out value normalization processing on the feature item set, and normalizing the read training sample data to be in the interval of [0,1 ]; establishing a neural network, setting training parameters of the BP neural network by the training parameters, and training; and reading a test sample, inputting the test sample into a neural network for testing, analyzing a result, counting the accuracy of sample identification, and generating a feature item set reference if the accuracy of sample identification is low.
The method and the system for collecting the service system data can intelligently correlate, fuse, deduce and calculate the data and the like, identify the true accuracy degree of the data and the correlation weight among the data, and reduce the manual retrieval and calculation of a large amount of data by the bidding audit supervisor in the audit investigation process.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the invention. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.
FIG. 1 is a flow chart of a method of collection of business system data of one embodiment of the present application;
FIG. 2 is a flow chart of a method of collecting business system data according to a specific embodiment of the present application;
FIG. 3 is a block diagram of a business system data collection system according to one embodiment of the present application;
FIG. 4 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flowchart of a method for collecting business system data according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
s101: and interpreting internal data of the enterprise, and extracting all business data in a target business system based on a full data extraction technology, wherein the business data comprises data of a bid purchasing system, a supplier system and a financial system.
S102: and performing data analysis on the service data, and acquiring the strength and accuracy of the data association relationship through data relationship evidence reasoning operation.
In a specific embodiment, the obtaining of the strength and accuracy of the data association relationship through data relationship evidence reasoning operation specifically includes:
s21: determining sample data read from within a business system
Is divided into service intervalsCloth
Matching similarity, and obtaining the similarity sum a of all sample pairs n，j ；
S22: calculating to obtain single characteristic likelihood function table
Wherein, y n For each sample feature value class, δ n The number of samples;
s23: inputting the characteristic likelihood function table
Obtaining a reliability matrix table;
s24: fusing evidences under evidence reasoning rules to obtain characteristics
In category y n Degree of confidence in
S25: obtaining an Attribute X i Reliability of (2)
Wherein Q is i The representation can be directly based on the attribute X i The number of samples of a specific class is determined.
S103: and matching the service data based on the data characteristic items, and extracting value data. The method specifically comprises type matching, semantic matching and data quality matching.
S104: and comparing the types and the values of the value data by using a characteristic item set comparison engine, and persistently storing correct information. In a specific embodiment, the method specifically includes:
s41: taking the real sample data set as a test sample, taking the obtained data as a training sample, carrying out value normalization processing on the feature item set, and normalizing the read training sample data to be in the interval of [0,1 ];
s42: establishing a neural network, setting training parameters of the training parameters to the BP neural network, and training;
s43: and reading a test sample, inputting the test sample into a neural network for testing, analyzing a result, counting the accuracy of sample identification, and generating a feature item set reference if the accuracy of sample identification is low.
Fig. 2 is a flowchart illustrating a method for collecting business system data according to a specific embodiment of the present application, and as shown in fig. 2, the method specifically includes the following steps:
and (4) interpreting the internal data file of the enterprise according to the authorization of the internal system of the enterprise or through a Hook technology interface, and extracting all data in the target business system by using a full data extraction technology. The internal database of the enterprise comprises a bid purchasing system, a supplier system, a financial system, a contract system and the like.
And extracting the data of the business system in full, and presetting business association relation and data relation evidence reasoning operation in a full data analysis engine to deduce the strength and accuracy of the data association relation. The business association presetting can be manually input for standby in advance to construct a data evidence reasoning operation engine, and is specifically realized as follows: and reasoning the relation between the bid section and the suppliers according to the manually-entered association preset information, such as the bid section number in the project bid information as a reliable factor and the supplier number in the supplier information as an importance weight, and reasoning whether information association (such as source relation, bid-winning linking and the like) exists between similar suppliers by the suppliers, and so on. Wherein the definition: reliability factor r: the ability of an information source generating evidence e to provide an accurate assessment or solution to a given question is embodied, and is an inherent property of evidence; importance weight w: the relative importance of evidence e compared to other evidence is defined, depending on what evidence participates in the fusion, who uses the evidence, and the specific instance of evidence usage. The method specifically comprises the following steps:
step 1: determining a similarity value: to represent
Matching
For subsequent measurements of the relationship between attribute X and category Y:
wherein a is ij In order to be the degree of similarity,
for a span distribution (e.g. a bid price span),
the read sample data (such as the acquired bid price data from the business system) is obtained.
Step 2: and (4) solving the similarity sum: a is a n，j Is all sample pairs
Matching
While the sample belongs to y n The similarity of (a) and (b). Summing the step 1 phase identity groups to obtain the following table:
wherein, y n For each sample feature value class, δ n Is the number of samples.
And step 3: inputting the result of step 2
A single characteristic likelihood function table is obtained. Wherein, C n，j Is a list of likelihood function values for the feature.
And 4, step 4: will be described in detail3 result input
And obtaining a reliability matrix table. Wherein the content of the first and second substances,
the likelihood function table is accumulated to obtain the total sum, and then the total sum is calculated
And (5) a confidence matrix list.
And 5: fusing evidences under the evidence reasoning rule, using a new sample and the value of a certain characteristic X
In category y n The confidence in (2) is obtained by weighted sum calculation
And 6: and (4) performing evidence (feature fusion) based on an evidence reasoning rule to obtain fused probability distribution. The reliability factor obtaining method comprises the following steps:
in the classification problem, the reliability of evidence represents the classification capability of the attributes that generated the evidence. More reliable attribute X i More samples in the sample set can be unambiguously and independently classified. Under the attribute with high reliability, the attribute value intervals of different classes have relatively small overlap. Thus, information source X i The reliability of (d) can be defined as:
wherein Q is i The representation can be directly based on the attribute X i The larger the value of the number of samples of a specific class is, the higher the reliability of the attribute is. And it can be seen from the above equation that the reliability of the other attributes is measured by comparison with the most reliable attribute.
And generating or supplementing bidding purchase data characteristic items through the steps, matching full-scale data based on the data characteristic items, and extracting value data. The characteristic items comprise standing item data items, bidding data items, supplier data items, bidding data items, bid evaluation data items, expert review data items, bid winning data items, financial data items in all stages and the like. And extracting value data based on the feature item matching, wherein the value data comprises project information, bidding section information, bid inviting information, bid purchasing information, bid information, primary bid evaluation, secondary bid evaluation, tertiary bid evaluation, bid winning information, financial information and the like. The matching mode comprises type matching, semantic matching and data quality matching. And storing the extracted value data into a formulation medium for training a data feature item set comparison training engine.
Through data item set comparison training, real sample data are input into a feature item set comparison engine to compare types and values, and the method comprises the following specific steps:
(1) And taking the real sample data set as a test sample, and taking the obtained data as a training sample.
(2) And performing value normalization on the feature item set, and normalizing the read training sample data to be between 0,1.
(3) A neural network is created.
(4) And setting training parameters such as precision, times and fixed values of the training parameters of the BP neural network.
(5) Training is started to train the neural network.
(6) And reading the test data and testing.
(7) The test sample is read and then input into a neural network for testing.
(8) And analyzing the result, and counting the accuracy of sample identification.
(9) And if the accuracy rate of the statistical sample is low, generating a feature item set reference, so that the data relation evidence reasoning reference operation is facilitated.
Judging the comparison result of the characteristic item set comparison engine, judging whether each piece of information is correct, if so, storing the information persistently, and if the information item is distorted, returning to the step of analyzing the full data for the distorted object for retraining.
With continuing reference to FIG. 3, FIG. 3 illustrates a block diagram of a business system data collection system according to one embodiment of the present application, which, as shown in FIG. 3, includes a gross data extraction unit 301, a data analysis unit 302, a data matching unit 303, and a data storage unit 304. The full data extraction unit 301 is configured to interpret internal data of an enterprise, and extract all business data in a target business system based on a full data extraction technology, where the business data includes data of a bid and bid purchasing system, a supplier system, and a financial system; the data analysis unit 302 is configured to perform data analysis on the service data, and obtain the strength and accuracy of the data association relationship through data relationship evidence reasoning operation; the data matching unit 303 is configured to match the service data based on the data feature items, and extract value data; the data storage unit 304 is configured to compare the type and the value of the value data by using the feature item set comparison engine, and persistently store correct information.
Referring now to FIG. 4, shown is a block diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, the computer system includes a Central Processing Unit (CPU) 401 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input portion 406 including a keyboard, a mouse, and the like; an output section 407 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409 and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 401. It should be noted that the computer readable storage medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a deployment unit, an instruction processing unit, and a file access unit. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: interpreting internal data of an enterprise, and extracting all business data in a target business system based on a full data extraction technology, wherein the business data comprises data of a bidding purchasing system, a supplier system and a financial system; performing data analysis on the service data, and acquiring the strength and accuracy of the data association relationship through data relationship evidence reasoning operation; matching the service data based on the data characteristic items, and extracting value data; and comparing the types and the values of the value data by using a characteristic item set comparison engine, and persistently storing correct information.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A method for collecting service system data is characterized by comprising the following steps:
s1: interpreting internal data of an enterprise, and extracting all business data in a target business system based on a full data extraction technology, wherein the business data comprises data of a bid purchasing system, a supplier system and a financial system;
s2: performing data analysis on the service data, and acquiring the strength and accuracy of a data association relation through data relation evidence reasoning operation;
s3: matching the service data based on the data characteristic items, and extracting value data;
s4: and comparing the types and the values of the value data by using a characteristic item set comparison engine, and persistently storing correct information.
2. The method for collecting business system data according to claim 1, wherein the obtaining of the strength and accuracy of the data association relationship through the data relationship evidence reasoning operation in S2 specifically comprises:
s21: determining sample data read from within a business system
And distribution of service intervals
Matching similarity, and obtaining the similarity sum a of all sample pairs n,j ；
S22: calculating to obtain single characteristic likelihood function table
Wherein, y n For each sample feature value class, δ n The number of samples;
s23: inputting the characteristic likelihood function table
Obtaining a reliability matrix table;
s24: fusing evidences under evidence reasoning rules to obtain characteristics
In category y n Degree of confidence in
S25: obtaining an Attribute X i Reliability of (2)
Wherein Q is i Representation can be directly based on attribute X i The number of samples of a specific class is determined.
3. The method for collecting business system data according to claim 1, wherein the matching of the business data based on the data feature item in S3 specifically includes type matching, semantic matching, and data quality matching.
4. The method for collecting data of a business system according to claim 1, wherein the S4 specifically comprises:
s41: taking the real sample data set as a test sample, taking the obtained data as a training sample, carrying out value normalization processing on the feature item set, and normalizing the read training sample data to be in the interval of [0,1 ];
s42: establishing a neural network, setting training parameters of the training parameters to the BP neural network, and training;
s43: and reading a test sample, inputting the test sample into the neural network for testing, analyzing a result, counting the accuracy of sample identification, and generating a feature item set reference if the accuracy of sample identification is low.
5. The method of claim 4, wherein the training parameters comprise accuracy, times, and fixed values.
6. A computer-readable storage medium having one or more computer programs stored thereon, which when executed by a computer processor perform the method of any one of claims 1 to 5.
7. A system for collecting data for a business system, the system comprising:
a full data extraction unit: the system is configured to interpret internal data of an enterprise and extract all business data in a target business system based on a full-scale data extraction technology, wherein the business data comprises data of a bid and tender purchasing system, a supplier system and a financial system;
a data analysis unit: the configuration is used for carrying out data analysis on the service data and obtaining the strength and the accuracy of the data association relationship through data relationship evidence reasoning operation;
a data matching unit: the data matching system is configured to match the service data based on the data feature items and extract value data;
a data storage unit: the system is configured to compare the types and the values of the value data by using a feature item set comparison engine, and persistently store correct information.
8. The system for collecting data of business system according to claim 7, wherein the obtaining of the strength and accuracy of the data association relationship through the data relationship evidence reasoning operation in the data analysis unit specifically includes determining the read sample data
And distribution of service intervals
Matching similarity, and obtaining the similarity sum a of all sample pairs n,j (ii) a Calculating to obtain single characteristic likelihood function table
Wherein, y n For each sample eigenvalue class, δ n The number of samples; inputting the characteristic likelihood function table
Obtaining a reliability matrix table; fusing evidences under evidence reasoning rules to obtain characteristics
In category y n Degree of confidence in
Obtaining an Attribute X i Reliability of (2)
Wherein Q is i The representation can be directly based on the attribute X i The number of samples of a specific class is determined.
9. The system for collecting data of business system according to claim 7, wherein said data matching unit matches said business data based on data feature items specifically includes type matching, semantic matching and data quality matching.
10. The system of claim 7, wherein the comparing the type and the value of the value data in the data storage unit using a feature item set comparison engine specifically comprises: taking the real sample data set as a test sample, taking the obtained data as a training sample, carrying out value normalization processing on the characteristic item set, and normalizing the read training sample data to be in an interval of [0,1 ]; establishing a neural network, setting training parameters of the training parameters to the BP neural network, and training; and reading a test sample, inputting the test sample into the neural network for testing, analyzing a result, counting the accuracy of sample identification, and generating a feature item set reference if the accuracy of sample identification is low.
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