CN108768929B - Electronic device, credit investigation feedback message analysis method and storage medium - Google Patents
Electronic device, credit investigation feedback message analysis method and storage medium Download PDFInfo
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- CN108768929B CN108768929B CN201810309576.4A CN201810309576A CN108768929B CN 108768929 B CN108768929 B CN 108768929B CN 201810309576 A CN201810309576 A CN 201810309576A CN 108768929 B CN108768929 B CN 108768929B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/22—Parsing or analysis of headers
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
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- H04L67/564—Enhancement of application control based on intercepted application data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/26—Special purpose or proprietary protocols or architectures
Abstract
The invention relates to an electronic device, an analysis method of credit investigation feedback messages and a storage medium, wherein the method comprises the following steps: after sending credit investigation data to a credit investigation system, receiving a feedback message of the credit investigation system aiming at the credit investigation data; acquiring a file name of the feedback message, and acquiring a message type and a main body type of the feedback message according to the file name; if the message type is an abnormal feedback message, acquiring a corresponding predefined analysis model according to the main body type, analyzing the feedback message according to the acquired analysis model, and acquiring an analysis result. The invention can automatically analyze the credit investigation feedback message and improve the processing efficiency and the accuracy.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to an electronic device, a credit investigation feedback packet parsing method, and a storage medium.
Background
At present, after reporting credit investigation data, for example, after reporting the credit investigation data to a chinese people bank, a financial institution receives a corresponding feedback message, where the feedback message includes feedback information of the reported credit investigation data, the feedback information is feedback of a credit investigation system to the reported credit investigation data, if the reported credit investigation data recorded by the feedback information is normal, the financial institution does not need to process the reported credit investigation data, and if the reported credit investigation data recorded by the feedback information is abnormal, the financial institution analyzes a problem of the reported credit investigation data according to a record in the feedback information, for example, the reported credit investigation data is reported again after modifying wrong content. The existing method for processing feedback messages is usually a manual processing method, namely, specific feedback information is checked according to the feedback message specification, and then positioning and solving problems are carried out. Because the feedback message is a long string of numbers, the manual processing mode is time-consuming and labor-consuming and is easy to make mistakes.
Disclosure of Invention
The invention aims to provide an electronic device, a credit investigation feedback message analysis method and a storage medium, aiming at automatically analyzing a credit investigation feedback message and improving the processing efficiency and accuracy.
In order to achieve the above object, the present invention provides an electronic device, which includes a memory and a processor connected to the memory, wherein the memory stores a processing system capable of running on the processor, and when executed by the processor, the processing system implements the following steps:
a receiving step, after sending credit investigation data to a credit investigation system, receiving a feedback message of the credit investigation system aiming at the credit investigation data;
an obtaining step, namely obtaining a file name of the feedback message, and obtaining a message type and a main body type of the feedback message according to the file name;
and analyzing, namely if the message type is an abnormal feedback message, acquiring a corresponding predefined analysis model according to the main body type, analyzing the feedback message according to the acquired analysis model, and acquiring an analysis result.
Preferably, the acquiring step specifically includes:
acquiring a file name of the feedback message, acquiring character data of a preset position and a preset length in the file name, and acquiring a last digit in the file name;
and matching the acquired character data with a predefined relation table of the main body type and the character string to acquire the main body type matched with the character data, and analyzing whether the last digit in the file name is an abnormal identifier or a normal identifier to acquire the message type.
Preferably, the subject types include a guarantee subject, a lender subject, and an insurance subject.
Preferably, the step of analyzing the feedback packet according to the obtained analysis model to obtain an analysis result specifically includes:
dividing the message data in the feedback message according to the position and the position corresponding to each data item name in the analysis model, wherein the message data comprises message header data and message body data;
and associating each segment of divided message data with the data item name and the data item description of the segment of message data, and taking each segment of message data in the feedback message, the data item name and the data item description associated with each segment of message data as an analysis result of the feedback message.
In order to achieve the above object, the present invention further provides an analysis method of a credit investigation feedback message, where the analysis method of the credit investigation feedback message includes:
s1, after sending credit investigation data to a credit investigation system, receiving a feedback message of the credit investigation system aiming at the credit investigation data;
s2, acquiring the file name of the feedback message, and acquiring the message type and the main body type of the feedback message according to the file name;
and S3, if the message type is an abnormal feedback message, acquiring a corresponding predefined analysis model according to the main body type, and analyzing the feedback message according to the acquired analysis model to acquire an analysis result.
Preferably, the step S2 specifically includes:
acquiring a file name of the feedback message, acquiring character data of a preset position and a preset length in the file name, and acquiring a last digit in the file name;
and matching the acquired character data with a predefined relation table of the main body type and the character string to acquire the main body type matched with the character data, and analyzing whether the last digit in the file name is an abnormal identifier or a normal identifier to acquire the message type.
Preferably, the subject types include a guarantee subject, a lender subject, and an insurance subject.
Preferably, the step of analyzing the feedback packet according to the obtained analysis model to obtain an analysis result specifically includes:
dividing the message data in the feedback message according to the position and the position corresponding to each data item name in the analysis model, wherein the message data comprises message header data and message body data;
and associating each segment of divided message data with the data item name and the data item description of the segment of message data, and taking each segment of message data in the feedback message, the data item name and the data item description associated with each segment of message data as an analysis result of the feedback message.
Preferably, if there is error information in the analysis result, marking the segment of message data corresponding to the error information, and giving an alarm or reminding when the scanning task scans the error information.
The invention also provides a computer readable storage medium, wherein a processing system is stored on the computer readable storage medium, and when being executed by a processor, the processing system realizes the steps of the analysis method of the credit investigation feedback message.
The invention has the beneficial effects that: according to the feedback message of the credit investigation system, the message type and the main body type of the feedback message are obtained through the file name of the feedback message, if the message type is an abnormal feedback message, a corresponding predefined analysis model is obtained according to the main body type, the feedback message is analyzed according to the obtained analysis model, an analysis result is obtained, the credit investigation feedback message can be automatically analyzed, and the processing efficiency and the accuracy are improved.
Drawings
FIG. 1 is a schematic diagram of an alternative application environment according to various embodiments of the present invention;
fig. 2 is a flowchart illustrating an embodiment of a method for analyzing a credit investigation feedback message according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. 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 invention.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Fig. 1 is a schematic application environment diagram of an analysis method for credit investigation feedback message according to a preferred embodiment of the present invention. The application environment schematic diagram comprises a financial institution credit investigation reporting system, a credit investigation system and an electronic device 1. The electronic device 1 may interact with the credit investigation system via a network, near field communication technology or other suitable technology. After the credit investigation reporting system of the financial institution reports the credit investigation data to the credit investigation system, the credit investigation system analyzes the credit investigation data and feeds back the credit investigation data to the electronic device 1.
The electronic apparatus 1 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a command set or stored in advance. The electronic device 1 may be a computer, or may be a single network server, a server group composed of a plurality of network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing, where cloud computing is one of distributed computing and is a super virtual computer composed of a group of loosely coupled computers.
In the present embodiment, the electronic device 1 may include, but is not limited to, a memory 11, a processor 12, and a network interface 13, which are communicatively connected to each other through a system bus, wherein the memory 11 stores a processing system operable on the processor 12. It is noted that fig. 1 only shows the electronic device 1 with components 11-13, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
The storage 11 includes a memory and at least one type of readable storage medium. The memory provides cache for the operation of the electronic device 1; the readable storage medium may be a non-volatile storage medium such as flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the readable storage medium may be an internal storage unit of the electronic apparatus 1, such as a hard disk of the electronic apparatus 1; in other embodiments, the non-volatile storage medium may also be an external storage device of the electronic apparatus 1, such as a plug-in hard disk provided on the electronic apparatus 1, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. In this embodiment, the readable storage medium of the memory 11 is generally used for storing an operating system and various types of application software installed in the electronic device 1, for example, program codes of a processing system in an embodiment of the present invention. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is generally configured to control the overall operation of the electronic device 1, such as performing data interaction or communication related control and processing with the credit investigation system. In this embodiment, the processor 12 is configured to run the program code stored in the memory 11 or process data, for example, run a processing system.
The network interface 13 may comprise a wireless network interface or a wired network interface, and the network interface 13 is generally used for establishing a communication connection between the electronic apparatus 1 and other electronic devices. In this embodiment, the network interface 13 is mainly used to connect the electronic device 1 with a credit investigation system, and establish a data transmission channel and a communication connection between the electronic device 1 and the credit investigation system.
The processing system is stored in the memory 11 and includes at least one computer readable instruction stored in the memory 11, which is executable by the processor 12 to implement the method of the embodiments of the present application; and the at least one computer readable instruction may be divided into different logic blocks depending on the functions implemented by the respective portions.
In the invention, after the credit investigation system (such as a pedestrian) receives credit investigation data reported by a financial institution, the credit investigation data is analyzed to obtain whether the credit investigation data meets a preset specification or format, and corresponding feedback is made to the financial institution. The financial institution formulates different analysis models according to the types of the business bodies, analyzes the feedback message according to the analysis models when receiving the feedback message, does not need human eyes to identify a series of numbers in the feedback message, and improves the processing efficiency and accuracy of the feedback message.
In one embodiment, the processing system described above, when executed by the processor 12, performs the following steps:
a receiving step, after sending credit investigation data to a credit investigation system, receiving a feedback message of the credit investigation system aiming at the credit investigation data;
an obtaining step, namely obtaining a file name of the feedback message, and obtaining a message type and a main body type of the feedback message according to the file name;
the difference is that the file name of the feedback message is one more bit than the file name of the reported credit investigation data, namely the last bit is more. The file name format and the characteristics of the feedback message are as follows: 1. the file name is 28 bits in length and consists of capital English letters and numbers, and the prefixes of the file names before and after compression encryption are consistent; 2. the file name uniquely identifies one feedback message and is not repeated with the file names of all the previous feedback messages.
The composition structure of the file name of the feedback message comprises 28 bits, wherein the 1 st to 27 th bits are the file name of credit investigation data, and the 28 th bit is an identification bit:
1 st to 14 th: a financial institution code representing the credit data submission;
15 th to 20 th: the date and month of the credit investigation data transmission;
21 st to 23 th: the serial number of the running water for representing credit investigation data is composed of numbers from 0 to 9 and capital letters A to Z;
position 24: category of the credit data: 1 is normal credit investigation data;
25 th to 27 th: the supplementary bits of serial number of credit investigation data stream are composed of numbers of 0-9 and capital letters A-Z.
Position 28: and the identification bit is a number 1 and then represents an abnormal identification.
For the file name of the feedback message, the suffix of the file name before compression and encryption is "txt", and the suffix of the file name after compression and encryption is "enc". For example, the message name of the credit and debit credit feedback message is n10155840h000120171126a10001.txt, and the message name of the credit and debit feedback message is n10156530h007720171104010001. txt.
In an embodiment, the acquiring step specifically includes: acquiring a file name of the feedback message, acquiring character data of a preset position and a preset length in the file name, and acquiring a last digit in the file name; and matching the acquired character data with a predefined relation table of the main body type and the character string to acquire the main body type matched with the character data, and analyzing whether the last digit in the file name is an abnormal identifier or a normal identifier to acquire the message type.
The subject types include a guarantee subject, a loan subject, and an insurance subject. By obtaining the character data with the length of 14 th and the position of 1 st to 14 th in the file name of the feedback message, the character data correspond to the financial institution code (i.e. the prefix of the message name), the financial institution code represents the corresponding main body type, and the character data is matched with the predefined relation table of the main body type and the character string to obtain the main body type matched with the character data, for example, in the relation table, the main body types of N10155840H0001 and N10156530H0077 are small credit main bodies.
And analyzing, namely if the message type is an abnormal feedback message, acquiring a corresponding predefined analysis model according to the main body type, analyzing the feedback message according to the acquired analysis model, and acquiring an analysis result.
If the message type is an abnormal feedback message, the financial institution needs to make corresponding adjustment aiming at the credit investigation data, and the feedback message fed back is firstly analyzed according to the analysis model.
In an embodiment, the step of analyzing the feedback packet according to the obtained analysis model to obtain an analysis result specifically includes: dividing the message data in the feedback message according to the position and the position corresponding to each data item name in the analysis model, wherein the message data comprises message header data and message body data; and associating each segment of divided message data with the data item name and the data item description of the segment of message data, and taking each segment of message data in the feedback message, the data item name and the data item description associated with each segment of message data as an analysis result of the feedback message.
The analysis model comprises a model for analyzing a message header and a model for analyzing a message body. In one embodiment, the model for parsing the body header of a lender is shown in table 1 below, and the model for parsing the body of a lender is shown in table 2 below:
TABLE 1
TABLE 2
For the feedback message of the lender subject, in the header of the message, for example, the data with the 1 st to 3 rd bit and the length of 3, the name of the corresponding data item is the message format version number, and the data item is described as "the format is n.n", which means the version number of the message format established by the credit investigation institution currently used.
For the feedback message of the lender, in the message body, for example, the data with the length of 27 at the 1 st to 27 th bits corresponds to the data item name of the error message file name, and the data item is described as the file name of the message where the error record exists.
In one embodiment, the model for analyzing the header of the vouching-for body is shown in table 3 below, and the model for analyzing the body of the vouching-for body is shown in table 4 below:
TABLE 3
TABLE 4
In the feedback message of the vouching subject, for example, in the 1 st bit data with length of 1, the name of the corresponding data item is the application system code, and the data item is described as "application system to which the file is applied, 1-enterprise credit system" in the header of the message.
For the feedback message of the vouching subject, in the message body, for example, data with 1 st to 14 th bit and length of 14, the corresponding data item name is the vouching authority code, and the data item is described as the "vouching authority code in the error information record".
In one embodiment, the processing system, when executed by the processor, further performs the steps of: if the analysis result has error information, marking the section of message data corresponding to the error information, and giving an alarm or reminding when the scanning task scans the error information.
Wherein, the analysis result refers to all information analyzed by the feedback message according to the analysis model, if the analyzed error information indicates that the credit report is wrong, and the data that specifically reports the mistake has a problem, then a field is marked as reporting failure, for example: when status is 2, error is 3014, and error is, the analyzed error information indicates that the datagram is incorrect, the scanning task scans the flag "status is 2" and the error code "3014", indicating that there is a flag error information, and the description of the error information "error is: for the same guarantee contract, the business changes which occur in a day must be combined into one information record for reporting. When the error information is scanned by the scanning task, an alarm or a prompt is given, for example, the error information is fed back to a relevant person through an email to pay attention to and modify the error information, so that the error information is conveniently and quickly processed, and the processing efficiency is further improved.
Compared with the prior art, the method and the device have the advantages that for the feedback message of the credit investigation system, the message type and the main body type of the feedback message are firstly obtained through the file name of the feedback message, if the message type is an abnormal feedback message, the corresponding predefined analysis model is obtained according to the main body type, the feedback message is analyzed according to the obtained analysis model to obtain the analysis result, the credit investigation feedback message can be automatically analyzed, and the processing efficiency and the accuracy are improved.
As shown in fig. 2, fig. 2 is a schematic flow chart of an embodiment of an analysis method for a credit investigation feedback message, where the analysis method for the credit investigation feedback message includes the following steps:
step S1, after sending credit investigation data to a credit investigation system, receiving a feedback message of the credit investigation system aiming at the credit investigation data;
step S2, obtaining the file name of the feedback message, and obtaining the message type and the main body type of the feedback message according to the file name;
the difference is that the file name of the feedback message is one more bit than the file name of the reported credit investigation data, namely the last bit is more. The file name format and the characteristics of the feedback message are as follows: 1. the file name is 28 bits in length and consists of capital English letters and numbers, and the prefixes of the file names before and after compression encryption are consistent; 2. the file name uniquely identifies one feedback message and is not repeated with the file names of all the previous feedback messages.
The composition structure of the file name of the feedback message comprises 28 bits, wherein the 1 st to 27 th bits are the file name of credit investigation data, and the 28 th bit is an identification bit:
1 st to 14 th: a financial institution code representing the credit data submission;
15 th to 20 th: the date and month of the credit investigation data transmission;
21 st to 23 th: the serial number of the running water for representing credit investigation data is composed of numbers from 0 to 9 and capital letters A to Z;
position 24: category of the credit data: 1 is normal credit investigation data;
25 th to 27 th: the supplementary bits of serial number of credit investigation data stream are composed of numbers of 0-9 and capital letters A-Z.
Position 28: and the identification bit is a number 1 and then represents an abnormal identification.
For the file name of the feedback message, the suffix of the file name before compression and encryption is "txt", and the suffix of the file name after compression and encryption is "enc". For example, the message name of the credit and debit credit feedback message is n10155840h000120171126a10001.txt, and the message name of the credit and debit feedback message is n10156530h007720171104010001. txt.
In an embodiment, the acquiring step specifically includes: acquiring a file name of the feedback message, acquiring character data of a preset position and a preset length in the file name, and acquiring a last digit in the file name; and matching the acquired character data with a predefined relation table of the main body type and the character string to acquire the main body type matched with the character data, and analyzing whether the last digit in the file name is an abnormal identifier or a normal identifier to acquire the message type.
The subject types include a guarantee subject, a loan subject, and an insurance subject. By obtaining the character data with the length of 14 th and the position of 1 st to 14 th in the file name of the feedback message, the character data correspond to the financial institution code (i.e. the prefix of the message name), the financial institution code represents the corresponding main body type, and the character data is matched with the predefined relation table of the main body type and the character string to obtain the main body type matched with the character data, for example, in the relation table, the main body types of N10155840H0001 and N10156530H0077 are small credit main bodies.
Step S3, if the message type is an abnormal feedback message, acquiring a predefined parsing model according to the body type, and parsing the feedback message according to the acquired parsing model to acquire a parsing result.
If the message type is an abnormal feedback message, the financial institution needs to make corresponding adjustment aiming at the credit investigation data, and the feedback message fed back is firstly analyzed according to the analysis model.
In an embodiment, the step of analyzing the feedback packet according to the obtained analysis model to obtain an analysis result specifically includes: dividing the message data in the feedback message according to the position and the position corresponding to each data item name in the analysis model, wherein the message data comprises message header data and message body data; and associating each segment of divided message data with the data item name and the data item description of the segment of message data, and taking each segment of message data in the feedback message, the data item name and the data item description associated with each segment of message data as an analysis result of the feedback message.
The analysis model comprises a model for analyzing a message header and a model for analyzing a message body. In one embodiment, the model for parsing the body header of the lender is shown in table 1 above, and the model for parsing the body of the lender is shown in table 2 above: for the feedback message of the lender subject, in the header of the message, for example, the data with the 1 st to 3 rd bit and the length of 3, the name of the corresponding data item is the message format version number, and the data item is described as "the format is n.n", which means the version number of the message format established by the credit investigation institution currently used.
For the feedback message of the lender, in the message body, for example, the data with the length of 27 at the 1 st to 27 th bits corresponds to the data item name of the error message file name, and the data item is described as the file name of the message where the error record exists.
In one embodiment, the model for analyzing the header of the security principal is shown in table 3, and the model for analyzing the body of the security principal is shown in table 4: in the feedback message of the vouching subject, for example, in the 1 st bit data with length of 1, the name of the corresponding data item is the application system code, and the data item is described as "application system to which the file is applied, 1-enterprise credit system" in the header of the message.
For the feedback message of the vouching subject, in the message body, for example, data with 1 st to 14 th bit and length of 14, the corresponding data item name is the vouching authority code, and the data item is described as the "vouching authority code in the error information record".
In an embodiment, the method for analyzing the credit investigation feedback message further includes: if the analysis result has error information, marking the section of message data corresponding to the error information, and giving an alarm or reminding when the scanning task scans the error information.
Wherein, the analysis result refers to all information analyzed by the feedback message according to the analysis model, if the analyzed error information indicates that the credit report is wrong, and the data that specifically reports the mistake has a problem, then a field is marked as reporting failure, for example: when status is 2, error is 3014, and error is, the analyzed error information indicates that the datagram is incorrect, the scanning task scans the flag "status is 2" and the error code "3014", indicating that there is a flag error information, and the description of the error information "error is: for the same guarantee contract, the business changes which occur in a day must be combined into one information record for reporting. When the error information is scanned by the scanning task, an alarm or a prompt is given, for example, the error information is fed back to a relevant person through an email to pay attention to and modify the error information, so that the error information is conveniently and quickly processed, and the processing efficiency is further improved.
Compared with the prior art, the method and the device have the advantages that for the feedback message of the credit investigation system, the message type and the main body type of the feedback message are firstly obtained through the file name of the feedback message, if the message type is an abnormal feedback message, the corresponding predefined analysis model is obtained according to the main body type, the feedback message is analyzed according to the obtained analysis model to obtain the analysis result, the credit investigation feedback message can be automatically analyzed, and the processing efficiency and the accuracy are improved.
The invention also provides a computer readable storage medium, wherein a processing system is stored on the computer readable storage medium, and when being executed by a processor, the processing system realizes the steps of the analysis method of the credit investigation feedback message.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. An electronic device, comprising a memory and a processor connected to the memory, wherein the memory stores a processing system operable on the processor, and the processing system when executed by the processor implements the following steps:
a receiving step, after sending credit investigation data to a credit investigation system, receiving a feedback message of the credit investigation system aiming at the credit investigation data, wherein the feedback message is encrypted and pressurized, and prefixes of the file names of the feedback message before and after pressurization and encryption are consistent;
an obtaining step, namely obtaining a file name of the feedback message, and obtaining a message type and a main body type of the feedback message according to the file name;
analyzing, namely if the message type is an abnormal feedback message, acquiring a corresponding predefined analysis model according to the main body type, analyzing the feedback message according to the acquired analysis model, and acquiring an analysis result;
the step of obtaining specifically includes:
acquiring a file name of the feedback message, acquiring character data of a preset position and a preset length in the file name, and acquiring a last digit in the file name;
and matching the acquired character data with a predefined relation table of the main body type and the character string to acquire the main body type matched with the character data, and analyzing whether the last digit in the file name is an abnormal identifier or a normal identifier to acquire the message type.
2. The electronic device of claim 1, wherein the subject types include a guarantee subject, a lender subject, and an insurance subject.
3. The electronic device according to claim 2, wherein the step of parsing the feedback packet according to the obtained parsing model to obtain a parsing result specifically comprises:
dividing the message data in the feedback message according to the position and the position corresponding to each data item name in the analysis model, wherein the message data comprises message header data and message body data;
and associating each segment of divided message data with the data item name and the data item description of the segment of message data, and taking each segment of message data in the feedback message, the data item name and the data item description associated with each segment of message data as an analysis result of the feedback message.
4. A credit investigation feedback message analysis method is characterized in that the credit investigation feedback message analysis method comprises the following steps:
s1, after sending credit investigation data to a credit investigation system, receiving a feedback message of the credit investigation system aiming at the credit investigation data, wherein the feedback message is encrypted and pressurized, and prefixes of the feedback message before and after pressurization and encryption are consistent;
s2, acquiring the file name of the feedback message, and acquiring the message type and the main body type of the feedback message according to the file name;
s3, if the message type is an abnormal feedback message, acquiring a corresponding predefined analysis model according to the main body type, and analyzing the feedback message according to the acquired analysis model to acquire an analysis result;
the step S2 specifically includes:
acquiring a file name of the feedback message, acquiring character data of a preset position and a preset length in the file name, and acquiring a last digit in the file name;
and matching the acquired character data with a predefined relation table of the main body type and the character string to acquire the main body type matched with the character data, and analyzing whether the last digit in the file name is an abnormal identifier or a normal identifier to acquire the message type.
5. The method of claim 4, wherein the subject types include a guarantee subject, a loan subject, and an insurance subject.
6. The method for analyzing a credit investigation feedback message according to claim 5, wherein the step of analyzing the feedback message according to the obtained analysis model to obtain an analysis result specifically comprises:
dividing the message data in the feedback message according to the position and the position corresponding to each data item name in the analysis model, wherein the message data comprises message header data and message body data;
and associating each segment of divided message data with the data item name and the data item description of the segment of message data, and taking each segment of message data in the feedback message, the data item name and the data item description associated with each segment of message data as an analysis result of the feedback message.
7. The method for analyzing a credit investigation feedback message according to claim 6, further comprising, if there is error information in the analysis result, marking the segment of message data corresponding to the error information, and alarming or reminding when the scanning task scans the error information.
8. A computer-readable storage medium, wherein the computer-readable storage medium has a processing system stored thereon, and the processing system, when being executed by a processor, implements the steps of the method for parsing the credit investigation feedback message according to any of claims 4 to 7.
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| CN201810309576.4A CN108768929B (en) | 2018-04-09 | 2018-04-09 | Electronic device, credit investigation feedback message analysis method and storage medium |
| PCT/CN2018/102198 WO2019196304A1 (en) | 2018-04-09 | 2018-08-24 | Electronic apparatus, credit feedback message parsing method, and storage medium |
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| CN110909060B (en) * | 2019-11-26 | 2023-10-03 | 中信银行股份有限公司 | Data reporting method and system |
| CN110879787B (en) * | 2019-12-31 | 2024-01-30 | 中国银行股份有限公司 | Client testing method and system |
| CN111614647B (en) * | 2020-05-14 | 2022-06-07 | 中国银行股份有限公司 | Message detection and analysis method and device |
| CN111723019A (en) * | 2020-06-28 | 2020-09-29 | 深圳壹账通智能科技有限公司 | Interface debugging method and system |
| CN111858693A (en) * | 2020-08-04 | 2020-10-30 | 武汉众邦银行股份有限公司 | Feedback information analysis visualization method based on credit investigation report |
| CN111782718B (en) * | 2020-08-11 | 2023-12-29 | 支付宝(杭州)信息技术有限公司 | Plug-in data reporting system and data reporting method |
| CN112187829B (en) * | 2020-10-21 | 2022-10-11 | 中国工商银行股份有限公司 | Online transaction message processing method, device and system |
| CN112785411A (en) * | 2020-12-29 | 2021-05-11 | 平安消费金融有限公司 | Credit investigation data processing method, system, equipment and computer readable storage medium |
| CN117176507B (en) * | 2023-11-02 | 2024-02-23 | 上海鉴智其迹科技有限公司 | Data analysis method, device, electronic equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8583520B1 (en) * | 2012-10-24 | 2013-11-12 | Causam Holdings, LLC | System, method, and apparatus for settlement for participation in an electric power grid |
| CN104363131A (en) * | 2014-10-14 | 2015-02-18 | 国家电网公司 | Electric power communication protocol exception detection method based on dynamic extensible finite state |
| CN104979908A (en) * | 2015-06-25 | 2015-10-14 | 云南电网有限责任公司电力科学研究院 | Substation network online failure analysis method |
| CN107506451A (en) * | 2017-08-28 | 2017-12-22 | 泰康保险集团股份有限公司 | abnormal information monitoring method and device for data interaction |
| CN107707549A (en) * | 2017-09-30 | 2018-02-16 | 迈普通信技术股份有限公司 | A kind of device and method automatically extracted using feature |
| CN107818150A (en) * | 2017-10-23 | 2018-03-20 | 中国移动通信集团广东有限公司 | A kind of log audit method and device |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7610375B2 (en) * | 2004-10-28 | 2009-10-27 | Cisco Technology, Inc. | Intrusion detection in a data center environment |
| CN104378380A (en) * | 2014-11-26 | 2015-02-25 | 南京晓庄学院 | System and method for identifying and preventing DDoS attacks on basis of SDN framework |
| CN106408413A (en) * | 2016-09-23 | 2017-02-15 | 快睿登信息科技（上海）有限公司 | Multi-cycle installment decision making method and system |
| CN107665464B (en) * | 2017-09-18 | 2021-05-25 | 平安科技（深圳）有限公司 | Method, device and equipment for generating credit investigation message and computer readable storage medium |
-
2018
- 2018-04-09 CN CN201810309576.4A patent/CN108768929B/en active Active
- 2018-08-24 WO PCT/CN2018/102198 patent/WO2019196304A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8583520B1 (en) * | 2012-10-24 | 2013-11-12 | Causam Holdings, LLC | System, method, and apparatus for settlement for participation in an electric power grid |
| CN104363131A (en) * | 2014-10-14 | 2015-02-18 | 国家电网公司 | Electric power communication protocol exception detection method based on dynamic extensible finite state |
| CN104979908A (en) * | 2015-06-25 | 2015-10-14 | 云南电网有限责任公司电力科学研究院 | Substation network online failure analysis method |
| CN107506451A (en) * | 2017-08-28 | 2017-12-22 | 泰康保险集团股份有限公司 | abnormal information monitoring method and device for data interaction |
| CN107707549A (en) * | 2017-09-30 | 2018-02-16 | 迈普通信技术股份有限公司 | A kind of device and method automatically extracted using feature |
| CN107818150A (en) * | 2017-10-23 | 2018-03-20 | 中国移动通信集团广东有限公司 | A kind of log audit method and device |
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