CN114443632A - Intelligent conversion method and system for credit of credit bank and computer equipment - Google Patents
Intelligent conversion method and system for credit of credit bank and computer equipment Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/258—Data format conversion from or to a database
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Abstract
The application relates to the technical field of data processing, in particular to an intelligent conversion method and system for credit of a credit bank and computer equipment. The method comprises the following steps: acquiring access data, wherein the access data comprises a service type; identifying the type of the access data, and converting the access data into data to be processed with a uniform data format; and identifying the service type in the data to be processed, and converting the data to be processed into standard data according to a preset conversion standard corresponding to the service type. The method and the device solve the technical problems that the existing credit bank is inconsistent in access data standard and cannot realize data butt joint of different systems.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent conversion method and system for credit of a credit bank and computer equipment.
Background
The credit bank is a management mode which simulates or refers to the functional characteristics of the bank and enables students to freely select learning contents, learning time and learning places. Similar to the deposit mode of the retail deposit of the commercial bank, the learner can obtain scores by study of the learners in a sporadic way at ordinary times, the scores can be stored in the authorities authorized by relevant national departments like money, and corresponding academic calendars and non-academic certificates can be exchanged after certain standards are met. The credit bank stores credits, and any credit corresponds to a knowledge achievement with a certain value.
At present, the credit conversion technology of credit banks is imperfect, and the conversion problem exists in credits of different schools and different properties. For example, the higher algebraic score of Shanghai university of transportation is 1 to 4 scores, and the higher algebraic score of east China university is 6 scores, so that different schools have different score standards for the same course, and therefore, how to convert different learning results according to certain standards is a key problem for implementation of score banks.
Disclosure of Invention
Therefore, the embodiment of the application provides an intelligent conversion method and system for credit of a credit bank and computer equipment, which can solve the technical problems that the existing credit bank has inconsistent access data standards and cannot realize data butt joint of different systems, and the specific technical scheme content is as follows:
in a first aspect, an embodiment of the present application provides an intelligent conversion method for credit of a credit bank, where the method includes:
acquiring access data, wherein the access data comprises a service type;
identifying the type of the access data, and converting the access data into data to be processed with a uniform data format;
and identifying the service type in the data to be processed, and converting the data to be processed into standard data according to a preset conversion standard corresponding to the service type.
By adopting the technical scheme, after the access data are acquired, the access data are converted into a uniform format, and the unification of the data formats is realized on a system level, namely the data access of different systems is realized; and converting the data to be processed according to the preset conversion standard to realize data conversion, thereby solving the technical problem that the credit bank is difficult to implement due to the inconsistent standards of different education systems and further realizing the data access problem of different systems.
Preferably, the converting the access data into the data to be processed in the unified data format includes:
splitting the access data according to a field splitting rule corresponding to the type of the access data, obtaining required contents from the split fields, combining the required contents according to a uniform data format to form data to be processed, and establishing a blood relationship between the data to be processed and the access data.
By adopting the technical scheme, the access data is split according to the fields, whether the split fields are required contents is identified, data sorting is rapidly realized, the blood relationship of the data to be processed and the predicted access data is established, and data tracing is rapidly realized.
Preferably, the method further comprises:
and importing the data to be processed into a data pool for data cleaning, screening unqualified data, searching corresponding access data according to the blood relationship of the unqualified data, performing format conversion again according to a uniform data format, and generating an alarm mark.
By adopting the technical scheme, the data is cleaned, useless or error data in the data can be reduced, the data can be corrected according to the accessed data of the data to be processed with the blood relationship tracing error, and the corrected data generates a mark alarm mark to record error information.
Preferably, the method further comprises:
importing the reconverted data to be processed into a data pool for data cleaning again;
and if the data to be processed carrying the alarm mark is unqualified again, generating alarm information.
By adopting the technical scheme, when the data to be processed is marked as unqualified again in data cleaning, corresponding alarm information is generated and sent to the person who accesses the data and the staff of the corresponding system of the method for data correction and error debugging.
Preferably, the service types include academic education and non-academic education, the identifying the service types in the data to be processed, and the converting the data to be processed into the standard data according to the preset conversion standard corresponding to the service types includes:
if the service type is the academic education, acquiring subject information and subject scores in the data to be processed, and converting the subject information and the subject scores into standard data according to a preset conversion standard according to preset weight values and the subject scores corresponding to the subject information;
and if the service type is non-academic education, acquiring an achievement name in the data to be processed, scoring the data to be processed according to a preset scoring standard, and converting the score into standard data according to a preset weight value corresponding to the achievement name and a preset conversion standard.
By adopting the technical scheme, the data conversion is realized by adopting a processing mode meeting the requirement on the academic calendar data and the non-academic calendar data.
Preferably, the access data type comprises a structured data type and an unstructured data type;
splitting the access data according to a field splitting rule corresponding to the type of the access data, and combining the required contents acquired by the split fields according to a uniform data format to form to-be-processed data comprises the following steps:
if the access data type is an unstructured data type, acquiring a keyword of required information through semantic identification, and splitting the access data outside a field range corresponding to another keyword according to the maximum length of a field corresponding to the keyword; matching the required information possibly associated with the keyword according to the keyword information in the split field; and combining the successfully matched contents to form data to be processed.
By adopting the technical scheme, the unstructured data is processed according to semantic recognition.
Preferably, if the access data type is a structured data type, the access data is split according to a known structure, and required information is extracted and combined to form data to be processed.
In a second aspect, an embodiment of the present application provides an intelligent conversion system for credit of credit banks, where the system includes:
the data access module is used for acquiring access data, and the access data comprises a service type;
the conversion module is used for identifying the type of the access data and converting the access data into the data to be processed with a uniform data format;
and the processing module is used for identifying the service type in the data to be processed and converting the data to be processed into standard data according to a preset conversion standard corresponding to the service type.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of the intelligent conversion method for credit of credit bank when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps of the credit intelligent conversion method for credit banks.
In summary, compared with the prior art, the beneficial effects brought by the technical scheme provided by the embodiment of the present application at least include:
1. after the access data is acquired, the access data is converted into a uniform format, and the unification of the data format is realized on a system level, namely the data access of different systems is realized; the data to be processed is converted according to the preset conversion standard, and the data conversion is realized, so that the technical problem that the credit bank is difficult to implement due to the fact that standards of different education systems are inconsistent is solved, and the data access problem of different systems is further realized;
2. splitting the access data according to the fields, identifying whether the split fields are required contents or not, quickly realizing data sorting, establishing a blood relationship of the data to be processed and expected access data, and quickly realizing data tracing;
3. the data is cleaned, useless or error data in the data can be reduced, the data can be corrected according to the accessed data of the data to be processed with blood relationship tracing errors, and the corrected data generates a mark alarm mark to record error information.
Drawings
Fig. 1 is a schematic flowchart of an intelligent conversion method for credit of a credit bank according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of an intelligent conversion method for credit of a credit bank according to another embodiment of the present application.
Fig. 3 is a second schematic flow chart of an intelligent conversion method for credit of credit bank according to another embodiment of the present application.
Fig. 4 is a third flowchart of an intelligent conversion method for credit of credit bank according to another embodiment of the present application.
Fig. 5 is a fourth flowchart illustrating an intelligent conversion method for credit of a credit bank according to another embodiment of the present application.
Fig. 6 is a schematic structural diagram of an intelligent conversion system for credit of a credit bank according to an embodiment of the present application.
Detailed Description
The present embodiment is only for explaining the present application, and it is not limited to the present application, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present application.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" in the present application is only one kind of association relationship describing the associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, in the present application, the character "/" indicates that the preceding and following related objects are in an "or" relationship, unless otherwise specified.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
The technical scheme of the application aims to construct data standards (main data standards) for core data of the credit bank, including a time-of-learning standard, a credit standard, a learning account data standard, various types of learning achievement data (achievement data standards of various rules and non-rules are defined in detail according to types), a special rule data standard, a general rule data standard and the like, so that the technical problems existing in the credit bank implementation are solved, and the universality and feasibility of the credit bank are improved. According to the scheme, a data standard and data governance facing a credit bank are established according to a big data platform technology (such as an Aliyun big data cloud platform technology), and data of credit bank business scenes are collected and analyzed to construct an open data platform.
The big data governance platform for various data contents has the advantages that data only stores structured data in a traditional mode, association of the data and standards of the data are not defined and limited on a macroscopic level, and when the data platform is built, solutions are required to be provided for diversity (mainly structured and unstructured) of data storage of a credit bank, requirements of global data governance and the like.
Referring to fig. 1, in an embodiment of the present application, there is provided an intelligent conversion method for credit of credit bank, the main steps of the method are described as follows:
s1: acquiring access data, wherein the access data comprises a service type;
s2: identifying the type of the access data, and converting the access data into data to be processed with a uniform data format;
s3: and identifying the service type in the data to be processed, and converting the data to be processed into standard data according to a preset conversion standard corresponding to the service type.
Specifically, in this embodiment, the access data is learning data that needs to be converted into the same standard, for example, the access data includes that "the score of mathematics i such as the first academic period in 2018 of the university open in guangdong is 3 scores, and the access data is obtained through a unified examination at the end of the academic period", and the access data is a binary character describing this information, and may be other embodiments, which is not described herein again.
The type of the access data, namely the data format of the data packet, may be different for different educational systems, and after the data conversion, the access data is converted into a uniform data type first, thereby reducing errors occurring in the data conversion process.
The service type is the classification of the access data in the education system, for example, the classification of mathematics I such as first high scholarship in 2018 of college A who reads from open university in Guangdong is 3 points, and the service type which belongs to is academic education obtained through the unified examination at the end of the scholarship; the academic education comprises achievements, achievement time, obtaining modes and description fields.
After the access data are obtained, the access data are converted into a uniform format, data processing is facilitated, data loss is reduced, and the data to be processed are converted according to a preset conversion standard, so that the technical problem that the branch banks are difficult to implement due to the fact that standards of different education systems are not consistent is solved, and the data access problem of different systems is achieved.
Referring to fig. 2, further, the service types include academic education and non-academic education, and step S3 includes:
s31: if the service type is the academic education, acquiring subject information and subject scores in the data to be processed, and converting the subject information and the subject scores into standard data according to a preset conversion standard according to preset weight values and the subject scores corresponding to the subject information;
s32: and if the service type is non-academic education, acquiring an achievement name in the data to be processed, scoring the data to be processed according to a preset scoring standard, and converting the score into standard data according to a preset weight value corresponding to the achievement name and a preset conversion standard.
Specifically, in the present embodiment, the result name is a skill name recognized by a country such as a certificate or title of the non-academic education, and the subject information is a name of a subject for which the current score is obtained in the academic education.
The service types comprise academic education and non-academic education, and the access data of the non-academic education can be 'zhang for a primary engineer through review in 2019'; the non-academic education comprises the fields of result name, acquisition time, acquisition mode and result introduction.
Specifically, in this embodiment, the predetermined conversion standard is a standard for converting the data to be processed into the same data unit. And according to different service types, the preset conversion standards are different.
For example, in the system corresponding to the method of the present application, the subject information in the data to be processed is higher mathematics, in the method, the subject score standard reference of higher mathematics is 6 scores, the subject score of higher mathematics of a university is defined as 4 scores, the subject score of higher mathematics of B university is defined as 3 scores, the science of higher mathematics of a university corresponds to 4 scores, a higher mathematics of a university corresponds to 5 scores, a higher mathematics of B university corresponds to 6 scores, a higher mathematics of B university corresponds to 2 scores, and a higher mathematics of B university corresponds to 4/6 × 5; the standard data corresponding to higher mathematics of yellow certain output is 6/6 × 2. The weighted value is a preset value.
For example, in non-academic education, the corresponding score of the primary job title in the system corresponding to the application is 5 points, and the corresponding score of the primary engineer title or the primary teacher title belonging to the primary job title is 5 points; the weight value is set corresponding to the difficulty of examination by the primary engineer, where the weight value is determined by multiplying the proportion of persons examined by the primary engineer each year by a preset benchmarking value, for example, the preset benchmarking value is 10, the annual passing rate of the primary teacher is 20%, the annual passing rate of the primary engineer is 30%, the primary teacher's job corresponds to 10-10 × 20%, the primary engineer's job corresponds to 10-10 × 30%, the standard data output by the primary engineer's job is 5 × (10-10 × 30%), and the standard data output by the primary teacher's job is 5 × (10-10 × 20%). The weighted value is a preset value.
Through the setting of the embodiment, the conversion rule is set in advance according to the service type, so that when the data to be processed is processed, the data conversion can be quickly realized, and the conversion precision is higher.
Further, in the conversion standard between the academic education and the non-academic education, a preset grade is used as a standard reference, for example, six grades are set, the first grade is the lowest grade, and the sixth grade is the highest grade, when the standard is accumulated to a certain numerical value in the academic education, the grade is evaluated, and when the standard is accumulated to a certain numerical value in the non-academic education, the grade is evaluated, so that the unified standard between the academic education and the non-academic education is realized through the preset conversion rule.
Further, in another embodiment, the collected access data is preprocessed, calculated and stored through the Aliskiren cloud.
In the embodiment, structured access data are accessed to the Ali cloud through an API (application programming interface), a data platform in the Ali cloud is constructed through an ETL (Extract, Transform, Load), data required in the data platform are acquired from the API every day or regularly according to a method for establishing a data warehouse, a data format is adjusted to generate to-be-processed data, the to-be-processed data are extracted, cleaned, combined and loaded by the data platform in the data acquisition process, and the to-be-processed data keep the completeness of the data and the consistency of the data in the process. When the business data volume is too large, in order to prevent the pressure of the Mysql data warehouse from being too large, the business data is transferred to a database Hbase of a hadoop platform through a button.
And in the unstructured data part, the data are respectively collected and stored in HDFS and Hbase through a sensor interface, a web crawler tool, a stream processing program and the like.
In other embodiments, the predetermined conversion standard may be other embodiments, which are not described herein.
Referring to fig. 3, further, in another embodiment, converting the access data into the to-be-processed data in the unified data format includes:
s21: splitting the access data according to a field splitting rule corresponding to the type of the access data, obtaining required contents from the split fields, combining the required contents according to a uniform data format to form data to be processed, and establishing a blood relationship between the data to be processed and the access data.
Specifically, in this embodiment, formats of access data submitted by different institutions or systems are different, and for the same institution, when submitting data, the data is converted into a data format recognized by the institution, and in this application, a field splitting logic corresponding to the data of the institution is preset by the system. For example, for university a, the data composition is "head-information segment-tail", and the composition rule of the information segment is "school name-professional name-information owner name-subject name-academic scoring data-other description", in this embodiment, the logic of field splitting is: the method comprises the steps that information segments are identified by special character components at the head and the tail, different information components in the information segments are described by bytes with the same length, when the information segments are split, the information segments are cut according to the preset length, the information of each field can be obtained according to the sequence, and then the required information in the information segments are combined to form data to be processed; for different schools, the field lengths of the information with the same meaning may be different, when the conversion is performed, the information in the stored information field may be converted by using a digit larger than the maximum length of single information in data accessed by the system of the present application, for example, in the information field of university a, 100 characters describe information corresponding to the school, and in the information field of university B, 200 characters describe information corresponding to the school, so that if the school information is required information in the system of the present application, more than 200 characters describe school information, for example, 300 characters, so that the lengths of the information with the same meaning stored in university a and university B are the same, thereby facilitating subsequent identification and conversion. In other embodiments, an information start character and an information end character may be set in the information segment to separate information in the information segment.
The blood relationship between the data to be processed and the access data can be established by setting the same unique identifier in the data to be processed and the access data, wherein the unique identifier is the same as the unique identifier; in another implementation, the data to be processed and the access data may be stored in the same storage area, and the blood relationship may be established in the storage area.
In the scheme, the blood relationship between the data to be processed and the access data is established because errors may occur in the process of processing the information of the data to be processed and part of the information is lost, and the initial access data can be searched for through the blood relationship to perform data verification, correction and the like.
Further, after the access data is subjected to field splitting according to a preset field splitting rule, the split fields are matched with the required information one by one according to the sequence, after one field is matched with the required information, the matched required information is deleted from the required information to be matched, and then the next field according to the time sequence is matched with the current required information.
With the advance of the matching progress, the amount of required information for field matching is gradually reduced after the field matching, so that the rapid matching is realized, and the matching times are reduced.
Specifically, the matching of the required information mentioned in this embodiment is to match the field content with the required information, such as the content of a school, a subject, a score, and the like, so as to extract the required information in the access data to form the data to be processed.
Referring to fig. 4, further, the method further includes:
s4: and importing the data to be processed into a data pool for data cleaning, screening unqualified data, searching corresponding access data according to the blood relationship of the unqualified data, performing format conversion again according to a uniform data format, and generating an alarm mark.
Specifically, the data to be processed, which is accessed to the system and processed, is stored in the data pool, and the data in the data pool is cleaned. In other embodiments of the present application, there are other data cleaning manners, which are not described herein.
After the data is cleaned, the corresponding access data can be automatically traced back according to the blood relationship for unqualified data, and then the access data is processed again, and format conversion is performed again in a uniform data format.
Referring to fig. 5, further, the method further includes:
s5: importing the reconverted data to be processed into a data pool for data cleaning again;
s6: and if the data to be processed carrying the alarm mark is unqualified again, generating alarm information.
Further, the access data type includes a structured data type and an unstructured data type, and step S21 includes:
s211: if the access data type is an unstructured data type, acquiring a keyword of required information through semantic identification, and splitting the access data outside a field range corresponding to another keyword according to the maximum length of a field corresponding to the keyword; matching the required information possibly associated with the keyword according to the keyword information in the split field; and combining the successfully matched contents to form data to be processed.
Specifically, the semantic recognition may use existing semantic recognition software or a network, and is not limited herein.
And if the access data is unstructured data, the arrangement of information in the access data is different, the keywords in the access data are identified through semantics, the keywords are cut according to the maximum length of a field corresponding to the keywords before and after the keywords, then the fields are subjected to fine semantic identification, and a preset word bank formed by the content related to the keywords is matched to extract the required information.
For example, if the required information is school, the keyword is school, college, university, etc., and the data length of the keyword is generally within 200 characters, the fields formed by 200 characters before and after the keyword are cut and extracted, the lexicon corresponding to the school is preset, all the names of the school using the system of the method are arranged in the lexicon, and the lexicon is used for performing refined matching.
In the implementation process, for a section of access data, the cutting range of a certain keyword may coincide with that of other keywords, and the cutting length of the keyword is automatically adjusted according to the position of the keyword in the required information.
For example, the information required by the school is the school, the university, the college, and the like, and is generally located at the end of the required information, so that the range cut after the keyword is overlapped with the cutting range of other keywords, and the range of the latter keyword is mainly; and if the two keywords are both in the middle of the corresponding required information, an equal division mode is adopted.
The principle to be followed by the present embodiment is that the cut fields cannot contain two or more keywords, so as to avoid the situation of computing resource waste caused by matching repeated data.
Further, if the access data type is a structured data type, the access data is split according to a known structure, and required information is extracted and combined to form data to be processed.
For the structured data, setting a field splitting logic corresponding to the data of the service type in advance, and directly splitting the structured data.
Further, in another embodiment, the displayed data is provided with a right, that is, according to the logged-in identity information, the data content queried by the identity information card is identified, and the related data content is displayed.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Referring to fig. 6, in an embodiment of the present application, an intelligent conversion system for credit of credit bank is provided, and the intelligent conversion system for credit bank credit corresponds to the intelligent conversion method for credit bank credit in the above embodiment one by one. The intelligent conversion system for the credit of the credit bank comprises;
the data access module is used for acquiring access data, and the access data comprises a service type;
the conversion module is used for identifying the type of the access data and converting the access data into the data to be processed with a uniform data format;
and the processing module is used for identifying the service type in the data to be processed and converting the data to be processed into standard data according to a preset conversion standard corresponding to the service type.
All or part of each module of the intelligent conversion system for the credit of the credit bank can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment of the embodiments of the present application, a computer device is provided, which may be a server. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device may be implemented by any type of volatile or non-volatile storage devices, including but not limited to: magnetic disk, optical disk, EEPROM (Electrically-Erasable Programmable Read Only Memory), EPROM (Erasable Programmable Read Only Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), magnetic Memory, flash Memory, PROM (Programmable Read-Only Memory). The memory of the computer device provides an environment for the running of an operating system and computer programs stored within it. The network interface of the computer device is used for communicating with an external terminal through a network connection. When being executed by a processor, the computer program realizes the intelligent conversion method steps of the credit bank credit described in the embodiment.
In an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the intelligent conversion method steps of the credit bank credit described in the above embodiment. The computer-readable storage medium includes a ROM (Read-Only Memory), a RAM (Random-Access Memory), a CD-ROM (Compact Disc Read-Only Memory), a magnetic disk, a floppy disk, and the like.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the system described in the present application may be divided into different functional units or modules to implement all or part of the functions described above.
Claims (10)
1. An intelligent conversion method for credit of a credit bank is characterized by comprising the following steps:
acquiring access data, wherein the access data comprises a service type;
identifying the type of the access data, and converting the access data into data to be processed with a uniform data format;
and identifying the service type in the data to be processed, and converting the data to be processed into standard data according to a preset conversion standard corresponding to the service type.
2. The intelligent conversion method for credit of credit banks according to claim 1, wherein the conversion of the access data into the data to be processed with the uniform data format comprises:
splitting the access data according to a field splitting rule corresponding to the type of the access data, obtaining required contents from the split fields, combining the required contents according to a uniform data format to form data to be processed, and establishing a blood relationship between the data to be processed and the access data.
3. The intelligent conversion method for credit of credit bank as claimed in claim 2, wherein said method further comprises:
and importing the data to be processed into a data pool for data cleaning, screening unqualified data, searching corresponding access data according to the blood relationship of the unqualified data, performing format conversion again according to a uniform data format, and generating an alarm mark.
4. The intelligent conversion method for credit of credit bank as claimed in claim 3, wherein said method further comprises:
importing the reconverted data to be processed into a data pool for data cleaning again;
and if the data to be processed carrying the alarm mark is unqualified again, generating alarm information.
5. The intelligent conversion method for credit of credit banks according to claim 2, wherein the service types include credit education and non-credit education, the identifying the service type in the data to be processed, and the converting the data to be processed into the standard data according to the preset conversion standard corresponding to the service type includes:
if the service type is the academic education, acquiring subject information and subject scores in the data to be processed, and converting the subject information and the subject scores into standard data according to a preset conversion standard according to preset weight values and the subject scores corresponding to the subject information;
and if the service type is non-academic education, acquiring an achievement name in the data to be processed, scoring the data to be processed according to a preset scoring standard, and converting the score into standard data according to a preset weight value corresponding to the achievement name and a preset conversion standard.
6. The credit bank credit intelligent conversion method of claim 5, wherein the access data type comprises a structured data type and an unstructured data type;
splitting the access data according to a field splitting rule corresponding to the type of the access data, and combining the required contents acquired by the split fields according to a uniform data format to form to-be-processed data comprises the following steps:
if the type of the access data is an unstructured data type, acquiring a keyword of required information through semantic recognition, and splitting the access data outside a field range corresponding to another keyword according to the maximum length of a field corresponding to the keyword; matching the required information possibly associated with the keyword according to the keyword information in the split field; and combining the successfully matched contents to form data to be processed.
7. The intelligent conversion method for credit of credit banks according to claim 6, wherein if the type of the accessed data is a structured data type, the accessed data is split according to a known structure, and the required information is extracted and combined to form the data to be processed.
8. An intelligent conversion system for credit of credit bank, characterized in that the system comprises:
the data access module is used for acquiring access data, and the access data comprises a service type;
the conversion module is used for identifying the type of the access data and converting the access data into the data to be processed with a uniform data format;
and the processing module is used for identifying the service type in the data to be processed and converting the data to be processed into standard data according to a preset conversion standard corresponding to the service type.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the intelligent conversion method for credit of credit bank as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which when executed by a processor implements the steps of the intelligent conversion method for credit of credit bank as claimed in any one of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860698A (en) * | 2023-02-23 | 2023-03-28 | 广东开放大学（广东理工职业学院） | Credit bank management system based on block chain and cloud computing technology |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112016899A (en) * | 2020-09-01 | 2020-12-01 | 国家开放大学 | Block chain-based professional education credit bank creation method, device and system |
| CN112907407A (en) * | 2021-02-26 | 2021-06-04 | 浪潮云信息技术股份公司 | Block chain technology-based lifelong education credit accumulation method and credit banking system |
| CN113778947A (en) * | 2021-09-26 | 2021-12-10 | 杭州安恒信息技术股份有限公司 | Data import method, device and equipment of kafka stream processing platform |
-
2021
- 2021-12-31 CN CN202111671372.3A patent/CN114443632A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112016899A (en) * | 2020-09-01 | 2020-12-01 | 国家开放大学 | Block chain-based professional education credit bank creation method, device and system |
| CN112907407A (en) * | 2021-02-26 | 2021-06-04 | 浪潮云信息技术股份公司 | Block chain technology-based lifelong education credit accumulation method and credit banking system |
| CN113778947A (en) * | 2021-09-26 | 2021-12-10 | 杭州安恒信息技术股份有限公司 | Data import method, device and equipment of kafka stream processing platform |
Non-Patent Citations (1)
| Title |
|---|
| 卢志高: "面向社会生源的学分认定和转换模式研究", 《高等职业教育探索》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860698A (en) * | 2023-02-23 | 2023-03-28 | 广东开放大学（广东理工职业学院） | Credit bank management system based on block chain and cloud computing technology |
| CN115860698B (en) * | 2023-02-23 | 2023-04-28 | 广东开放大学（广东理工职业学院） | Bin bank management system based on blockchain and cloud computing technology |
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