File content abnormity monitoring method and system, computer equipment and storage medium

Abstract

The invention discloses a file content exception monitoring method and system, computer equipment and a storage medium, relates to the technical field of computers, and can solve the technical problems of slow discovery timeliness, low recognition accuracy and inflexible exception monitoring of file content changes at present. Comprising the steps of obtaining a credit report in a target format, identifying table field information in the credit report, and determining a to-be-monitored table field and a field value corresponding to the to-be-monitored table field from the table field information according to a preset table field positioning path; calling rule configuration information configured based on a rule engine, judging whether the rule configuration information is within rule effective time of the rule configuration information or not, and if yes, querying a preset monitoring rule corresponding to the to-be-monitored table field from the rule configuration information; and judging whether the credit report is abnormal or not according to the preset monitoring rule and the field value.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, system, computer equipment and storage medium for monitoring abnormal file content. Background Art

[0002] Credit reporting is a way to collect, organize, preserve and process the credit information of natural persons, legal persons and other organizations in accordance with the law. It includes personal credit reporting and corporate credit reporting. The credit reporting records the basic information of individuals / enterprises, credit information (whether there are bank loans, whether there are overdue payments, etc.), non-bank information, etc.

[0003] With the development of the credit economy, credit reports, as documents that record the credit history of individuals or companies, are crucial to the risk assessment of financial institutions. When handling leasing business, various indicators in the credit report can be used to conduct qualification review of the lessee, leasing method selection, repayment interest rate assessment, etc.

[0004] However, the management of credit reporting supervision is becoming increasingly strict, and the update status of the content displayed in the credit report is not transparent. There are problems such as slow detection of changes in the report content, low recognition accuracy, and insufficient flexibility in abnormal monitoring. Summary of the invention

[0005] In view of this, the present invention provides a file content anomaly monitoring method, system, computer device and storage medium, which can solve the current technical problems of slow discovery of file content changes, low recognition accuracy and insufficient flexibility of anomaly monitoring.

[0006] According to a first aspect of the present invention, a method for monitoring abnormal file content is provided, the method comprising:

[0007] Obtain a credit report in a target format, identify table field information in the credit report, and determine the table field to be monitored and the field value corresponding to the table field to be monitored from the table field information according to a preset table field location path;

[0008] Calling the rule configuration information configured based on the rule engine, determining whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, querying the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information;

[0009] Whether the credit report is abnormal is determined based on the preset monitoring rules and the field value.

[0010] According to a second aspect of the present invention, a file content abnormality monitoring system is provided, the system comprising:

[0011] Identification module, calling module, judgment module;

[0012] The identification module is used to obtain a credit report in a target format, identify table field information in the credit report, and determine the table field to be monitored and the field value corresponding to the table field to be monitored from the table field information according to a preset table field location path;

[0013] The calling module is used to call the rule configuration information based on the rule engine configuration, determine whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, query the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information;

[0014] The judgment module is used to judge whether the credit report is abnormal according to the preset monitoring rules and the field value.

[0015] According to a third aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned file content abnormality monitoring method is implemented.

[0016] According to the fourth aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned file content abnormality monitoring method when executing the program.

[0017] By means of the above technical scheme, the present invention provides a method, system, computer device and storage medium for monitoring abnormal file content. First, a credit report in a target format is obtained, table field information in the credit report is identified, and the table field to be monitored and the field value corresponding to the table field to be monitored are determined from the table field information according to a preset table field positioning path; then, rule configuration information configured based on a rule engine is called to determine whether the rule configuration information is within the rule effectiveness time of the rule configuration information. If so, the preset monitoring rule corresponding to the table field to be monitored is queried from the rule configuration information; finally, whether the credit report is abnormal is determined according to the preset monitoring rule and the field value. Through the technical solution of the present invention, on the one hand, the fields of the table to be monitored and the corresponding field values ​​are extracted from the credit report, and the rule configuration information is used to determine whether the credit report is abnormal, so that when the content of the credit report changes, it can be discovered in time, and the rule configuration information is based on the rule engine configuration, and can be flexibly configured as needed, thereby improving the flexibility of monitoring. On the other hand, what is identified is the credit report in the target format, that is, no matter what format, as long as it is not the target format, it is converted into the target format and then identified, avoiding data loss when parsing non-target formats, thereby improving the recognition accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation on the local application. In the drawings:

[0019] Figure 1 A schematic diagram of a process of monitoring a method for abnormal file content provided by an embodiment of the present invention is shown;

[0020] Figure 2 A schematic diagram showing a flow chart of another method for monitoring abnormal file content provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the structure of a file content abnormality monitoring system provided by an embodiment of the present invention is shown;

[0022] Figure 4 A schematic diagram showing the structure of another file content abnormality monitoring system provided by an embodiment of the present invention is shown;

[0023] Figure 5 is a schematic diagram of a structure of a computer device in one embodiment of the present invention;

[0024] Figure 6 It is another structural schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0026] This embodiment provides a method for monitoring abnormal file content. Figure 1 As shown, the method includes:

[0027] 101. Obtain a credit report in a target format, identify table field information in the credit report, and determine a table field to be monitored and a field value corresponding to the table field to be monitored from the table field information according to a preset table field location path.

[0028] Among them, there can be many formats of credit reports. One format is determined as the target format. For non-target formats, they need to be converted into the target format. When identifying table field information, credit reports in the target format are identified. In this way, the low accuracy of the recognition process caused by format changes can be avoided, which ultimately leads to the problem of low accuracy of file content abnormality monitoring.

[0029] For this embodiment, the table field information includes all the table fields in the credit report, and there is an inclusion relationship between some of the table fields. For example, table field 1 includes table field 11 and table field 12, and table field 11 includes table field 111 and table field 112. Therefore, when it is necessary to determine the table field to be monitored that needs to be monitored from all the table fields, as an implementation method, a preset table field positioning path is pre-set. For example, the table field to be monitored is table field 111 in table field 1-table field 11-table field 111, then the table field to be monitored in all the table fields can be located according to the preset table field positioning path, and the table field information includes the field value corresponding to the table field to be monitored, which is also determined.

[0030] 102. Call the rule configuration information based on the rule engine configuration to determine whether the rule configuration information is within the rule effective time of the rule configuration information. If so, query the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information.

[0031] 103. Determine whether the credit report is abnormal based on the preset monitoring rules and the field value.

[0032] Among them, the Rule Engine is a component embedded in the application, which separates business decisions from application code and uses predefined semantic modules (domain-specific language) to write business decisions (business rules), which can be configured and managed by users or developers when needed.

[0033] For steps 102 and 103 of the embodiment, the rule configuration information is configured through the rule engine, and the rule configuration information can be customized and updated at any time according to different needs, such as changing the fields of the table to be monitored and / or changing the preset monitoring rules, thereby ensuring that the latest rule configuration information is used when judging whether the credit report is abnormal (i.e., whether the content of the credit report has changed).

[0034] The present invention provides a method, system, computer device and storage medium for monitoring abnormal file content. First, a credit report in a target format is obtained, table field information in the credit report is identified, and the table field to be monitored and the field value corresponding to the table field to be monitored are determined from the table field information according to a preset table field positioning path; then, rule configuration information configured based on a rule engine is called to determine whether the rule configuration information is within the rule effective time of the rule configuration information. If so, the preset monitoring rule corresponding to the table field to be monitored is queried from the rule configuration information; finally, whether the credit report is abnormal is determined according to the preset monitoring rule and the field value. Through the technical solution of the present invention, on the one hand, the fields of the table to be monitored and the corresponding field values ​​are extracted from the credit report, and the rule configuration information is used to determine whether the credit report is abnormal, so that when the content of the credit report changes, it can be discovered in time, and the rule configuration information is based on the rule engine configuration, and can be flexibly configured as needed, thereby improving the flexibility of monitoring. On the other hand, what is identified is the credit report in the target format, that is, no matter what format, as long as it is not the target format, it is converted into the target format and then identified, avoiding data loss when parsing non-target formats, thereby improving the recognition accuracy.

[0035] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another file content abnormality monitoring method is provided, such as Figure 2 As shown, the method includes:

[0036] 201. Obtain an initial credit report in an initial format, determine whether the initial format is the same as a target format, and if the initial format is not the same as the target format, convert the initial format into the target format to obtain a credit report in the target format.

[0037] For this embodiment, if the initial format is the same as the target format, the initial credit report is determined as the credit report in the target format. The initial format may include hypertext markup language (html), pdf, json, and xml. The target format may be any one of html, pdf, json, and xml, and is not limited here. For example, if the initial format is pdf and the target format is pdf, then the initial format is the same as the target format.

[0038] As an implementation mode, if the initial format is different from the target format, the initial format is converted into the target format to obtain a credit report in the target format, including: if the initial format is a hypertext markup language format and the target format is a json format, the initial credit report is parsed into a document based on a hypertext markup language format parser; using the document object, the credit item data of the initial credit report is retrieved, wherein the credit item includes a table, a table body, a table row, a header cell and a cell; the credit item data is assigned to each corresponding attribute of java to obtain object data; the object data is converted into the json format to obtain a credit report in the json format.

[0039] Among them, Hypertext Markup Language is a standard markup language used to create and maintain web pages. It unifies the document format on the Internet through a series of tags, allowing scattered Internet resources to be connected into a logical whole.

[0040] The hypertext markup language format parser may be jsoup, which is a Java html parser that can directly parse a url address and html text content.

[0041] For example, first, obtain the initial credit report in the initial format (html format). The main tags are: : root element. : Contains the metadata of the document. <title> : Document title.< / title> : The main body of the document, used to store the user's credit item data. : Used to group block-level content in a document. Specifically, it groups and displays the user's basic personal information, information summary, credit transaction information details, public information details, and query records. 、 、 、 and : It is the defined credit item, corresponding to: table, table body, table row, table header cell and cell. It is used to list the credit item data of each credit item.

[0042] Then, the initial credit report in HTML format is parsed into a document through the Jsoup::parse method.

[0043] Furthermore, through the Document object (the document object allows us to access all elements in the HTML page from the script) methods such as Document::getElementsByTag and Document::getElementsByClass, the user's credit item data is retrieved and assigned to various attributes of the Java object.

[0044] Finally, convert the object data into json format.

[0045] 202. Obtain a credit report in a target format, identify table field information in the credit report, and determine a table field to be monitored and a field value corresponding to the table field to be monitored from the table field information according to a preset table field location path.

[0046] Among them, as an implementation method, natural language processing (NLP), optical character recognition (OCR), etc. can be used to identify the table field information in the credit report, which is not limited here.

[0047] 203. In response to the monitoring rule configuration instruction, receive the preset monitoring rules corresponding to each field of the table to be monitored, wherein the preset monitoring rules include: rule type, expected value content and the maximum number of times the preset monitoring rules are used, and the rule types include: equivalence comparison, dictionary comparison, digital range comparison and format comparison.

[0048] 204. Configure rule configuration information based on a rule engine, wherein the rule configuration information includes the preset monitoring rule corresponding to each of the to-be-monitored fields and the to-be-monitored table fields, and set a rule effective time of the rule configuration information.

[0049] For steps 203 and 204 of the embodiment, the rule configuration information is customized according to the monitoring requirements, and the monitoring rule configuration instruction is sent through the triggering of the visual interface. The monitoring rule configuration instruction includes the rule configuration information, that is, the preset monitoring rules corresponding to each field of the table to be monitored. Among them, the rule types are equal value comparison, dictionary comparison, digital range comparison and format comparison. Equal value comparison means that the field value needs to be equal to the expected value content, dictionary comparison means that the field value needs to belong to the dictionary enumeration listed in the expected value content, digital range comparison means that the field value needs to belong to the digital range listed in the expected value content, and format comparison means that the field value needs to match the regular expression corresponding to the expected value content.

[0050] Among them, the maximum number of uses of the preset monitoring rules refers to the upper limit of the number of times the preset monitoring rules are used to determine whether the credit report is abnormal.

[0051] 205. Call the rule configuration information based on the rule engine configuration to determine whether the rule configuration information is within the rule effective time of the rule configuration information. If so, query the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information.

[0052] 206. Determine whether the credit report is abnormal based on the preset monitoring rule and the field value.

[0053] It should be noted that in specific application scenarios, it is necessary to determine whether the content of the credit report has changed beyond the prescribed range. If so, the credit report is abnormal.

[0054] As an implementation mode, judging whether the credit report is abnormal according to the preset monitoring rule and the field value includes: obtaining the current number of times the preset monitoring rule has been used; judging whether the current number of times used is less than or equal to the maximum number of times used (automatically monitoring whether the preset monitoring rule needs to be used), and if so, then:

[0055] If the rule type is the equal value comparison, then determine whether the field value is equal to the expected value content, if not, the credit report is abnormal;

[0056] If the rule type is the dictionary comparison, it is determined whether the field value belongs to the dictionary enumeration of the expected value content, and if it does not belong to the dictionary enumeration of the expected value content, the credit report is abnormal;

[0057] If the rule type is the digital range comparison, then determine whether the field value belongs to the digital range listed in the expected value content, if not, then the credit report is abnormal;

[0058] If the rule type is the format comparison, it is determined whether the field value matches the regular expression corresponding to the expected value content. If not, the credit report is abnormal.

[0059] For example, under a user's credit transaction information details, there is an account list. The fields to be monitored in each account list include account name, account status, loan amount, and account opening date:

[0060]

[0061]

[0062] The process of determining whether the credit report is abnormal is as follows, taking the account status field to be monitored as an example:

[0063] 1) Locate a field to be monitored according to the configured preset table field location path: PERSONAL_CREDIT_TRADE_DETAIL#TRADE_ACCOUNT_LIST#ACCOU NT_STATUS;

[0064] 2)Rule type = "Dictionary comparison";

[0065] 3) The only dictionary enumerations that can be obtained by looking up the dictionary table and listing the expected value content are ["normal", "overdue", "settled", "bad debt", "transfer out", "insufficient collateral", "forced liquidation", "judicial recovery"];

[0066] 4) Determine whether "freeze" is in the dictionary enumeration of expected value contents.

[0067] Therefore, if the account status field in the monitored table is abnormal, as long as there is an abnormal field in the credit report, the credit report is abnormal, and the credit report abnormal information (including the abnormal field in the monitored table and its corresponding abnormal field value) can be sent to the designated terminal.

[0068] By supporting content parsing and content monitoring of reports in various formats, timely capture and early warning of changes in credit reports are achieved, avoiding misjudgment of downstream business rule models due to format changes or content changes of important indicators, thereby avoiding the handling of abnormal business.

[0069] The present invention provides a method, system, computer device and storage medium for monitoring abnormal file content. First, a credit report in a target format is obtained, table field information in the credit report is identified, and the table field to be monitored and the field value corresponding to the table field to be monitored are determined from the table field information according to a preset table field positioning path; then, rule configuration information configured based on a rule engine is called to determine whether the rule configuration information is within the rule effective time of the rule configuration information. If so, the preset monitoring rule corresponding to the table field to be monitored is queried from the rule configuration information; finally, whether the credit report is abnormal is determined according to the preset monitoring rule and the field value. Through the technical solution of the present invention, on the one hand, the fields of the table to be monitored and the corresponding field values ​​are extracted from the credit report, and the rule configuration information is used to determine whether the credit report is abnormal, so that when the content of the credit report changes, it can be discovered in time, and the rule configuration information is based on the rule engine configuration, and can be flexibly configured as needed, thereby improving the flexibility of monitoring. On the other hand, what is identified is the credit report in the target format, that is, no matter what format, as long as it is not the target format, it is converted into the target format and then identified, avoiding data loss when parsing non-target formats, thereby improving the recognition accuracy.

[0070] Further, as Figure 1 and Figure 2 The specific implementation of the method shown in the figure, the embodiment of the present invention provides a file content abnormality monitoring system, such as Figure 3 As shown, the system includes: an identification module, a calling module, and a judgment module;

[0071] An identification module, used for obtaining a credit report in a target format, identifying table field information in the credit report, and determining a table field to be monitored and a field value corresponding to the table field to be monitored from the table field information according to a preset table field location path;

[0072] A calling module, used for calling the rule configuration information based on the rule engine configuration, judging whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, querying the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information;

[0073] A judgment module is used to judge whether the credit report is abnormal according to the preset monitoring rules and the field value.

[0074] In specific application scenarios, such as Figure 4 As shown, a file content anomaly system, the system also includes: a format conversion module, which can be specifically used to obtain an initial credit report in an initial format, determine whether the initial format is the same as a target format, if the initial format is the same as the target format, then determine the initial credit report as a credit report in the target format, if the initial format is not the same as the target format, then convert the initial format into the target format to obtain a credit report in the target format.

[0075] Correspondingly, in order to convert the initial format into the target format if the initial format is different from the target format and obtain the credit report in the target format, the format conversion module can be specifically used to parse the initial credit report into a document based on the hypertext markup language format parser if the initial format is the hypertext markup language format and the target format is the json format; use the document object to retrieve the credit item data of the initial credit report, wherein the credit item includes a table, a table body, a table row, a header cell and a cell; assign the credit item data to the corresponding properties of java to obtain object data; convert the object data into the json format to obtain the credit report in the json format.

[0076] In specific application scenarios, such as Figure 4 As shown, a file content anomaly system, the system also includes: a rule engine configuration module, which can be specifically used to respond to monitoring rule configuration instructions, receive preset monitoring rules corresponding to each field in the table to be monitored, configure the field in the table to be monitored and the corresponding preset monitoring rules, wherein the preset monitoring rules include: rule type, expected value content and the maximum number of uses of the preset monitoring rules, the rule types include: equivalence comparison, dictionary comparison, digital range comparison and format comparison, based on the rule engine configuration rule configuration information, wherein the rule configuration information includes each field to be monitored and the preset monitoring rules corresponding to the field in the table to be monitored, and sets the rule effective time of the rule configuration information.

[0077] Correspondingly, in order to determine whether the credit report is abnormal according to the preset monitoring rules and the field value, the judgment module can be specifically used to obtain the current number of times the preset monitoring rules have been used; determine whether the current number of times used is less than or equal to the maximum number of times used, and if so, then: if the rule type is the equal value comparison, determine whether the field value is equal to the expected value content, if not equal, the credit report is abnormal; if the rule type is the dictionary comparison, determine whether the field value belongs to the dictionary enumeration listed in the expected value content, if not, the credit report is abnormal; if the rule type is the digital range comparison, determine whether the field value belongs to the digital range listed in the expected value content, if not, the credit report is abnormal; if the rule type is the format comparison, determine whether the field value matches the regular expression corresponding to the expected value content, if not, the credit report is abnormal.

[0078] The present invention provides a method, system, computer device and storage medium for monitoring abnormal file content. First, a credit report in a target format is obtained, table field information in the credit report is identified, and the table field to be monitored and the field value corresponding to the table field to be monitored are determined from the table field information according to a preset table field positioning path; then, rule configuration information configured based on a rule engine is called to determine whether the rule configuration information is within the rule effective time of the rule configuration information. If so, the preset monitoring rule corresponding to the table field to be monitored is queried from the rule configuration information; finally, whether the credit report is abnormal is determined according to the preset monitoring rule and the field value. Through the technical solution of the present invention, on the one hand, the fields of the table to be monitored and the corresponding field values ​​are extracted from the credit report, and the rule configuration information is used to determine whether the credit report is abnormal, so that when the content of the credit report changes, it can be discovered in time, and the rule configuration information is based on the rule engine configuration, and can be flexibly configured as needed, thereby improving the flexibility of monitoring. On the other hand, what is identified is the credit report in the target format, that is, no matter what format, as long as it is not the target format, it is converted into the target format and then identified, avoiding data loss when parsing non-target formats, thereby improving the recognition accuracy.

[0079] For the specific definition of the file content anomaly monitoring system, please refer to the definition of the file content anomaly monitoring method above, which will not be repeated here. Each module in the above-mentioned file content anomaly monitoring system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0080] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a file content abnormality monitoring method server side.

[0081] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input system connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, the functions or steps of the client side of a file content abnormality monitoring method are implemented.

[0082] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0083] Obtain a credit report in a target format, identify table field information in the credit report, and determine the table field to be monitored and the field value corresponding to the table field to be monitored from the table field information according to a preset table field location path;

[0084] Calling the rule configuration information configured based on the rule engine, determining whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, querying the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information;

[0085] Whether the credit report is abnormal is determined based on the preset monitoring rules and the field value.

[0086] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0087] Obtain a credit report in a target format, identify table field information in the credit report, and determine the table field to be monitored and the field value corresponding to the table field to be monitored from the table field information according to a preset table field location path;

[0088] Calling the rule configuration information configured based on the rule engine, determining whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, querying the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information;

[0089] Whether the credit report is abnormal is determined based on the preset monitoring rules and the field value.

[0090] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0091] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0092] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0093] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for monitoring abnormal file content, characterized in that: The method comprises: Obtain a credit report in a target format, identify table field information in the credit report, and determine the table field to be monitored and the field value corresponding to the table field to be monitored from the table field information according to a preset table field location path; Calling the rule configuration information configured based on the rule engine, determining whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, querying the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information; Whether the credit report is abnormal is determined based on the preset monitoring rules and the field value.

2. The method according to claim 1, characterized in that Before obtaining the credit report in the target format, the method further includes: Obtaining an initial credit report in an initial format, and determining whether the initial format is the same as a target format; If the initial format is the same as the target format, determining the initial credit report as a credit report in the target format; If the initial format is different from the target format, the initial format is converted into the target format to obtain a credit report in the target format.

3. The method according to claim 2, characterized in that If the initial format is different from the target format, converting the initial format into the target format to obtain a credit report in the target format includes: If the initial format is a hypertext markup language format and the target format is a json format, the initial credit report is parsed into a document based on a hypertext markup language format parser; Using the document object, retrieve the credit item data of the initial credit report, wherein the credit item includes a table, a table body, a table row, a table header cell, and a cell; Assign the credit item data to the corresponding attributes of Java to obtain object data; The object data is converted into the json format to obtain a credit report in the json format.

4. The method according to claim 1, characterized in that: In calling the rule configuration information configured based on the rule engine, determining whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, before querying the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information, the method further includes: In response to the monitoring rule configuration instruction, receiving the preset monitoring rule corresponding to each field of the table to be monitored, wherein the preset monitoring rule includes: rule type, expected value content and maximum number of times the preset monitoring rule is used, and the rule type includes: equal value comparison, dictionary comparison, digital range comparison and format comparison; Based on the rule engine, the rule configuration information is configured, wherein the rule configuration information includes the preset monitoring rule corresponding to each of the to-be-monitored fields and the to-be-monitored table fields, and the rule effective time of the rule configuration information is set.

5. The method according to claim 4, characterized in that The determining whether the credit report is abnormal according to the preset monitoring rule and the field value includes: Obtain the current number of times the preset monitoring rule has been used; Determine whether the current number of times used is less than or equal to the maximum number of times used. If so, then: If the rule type is the equal value comparison, then determine whether the field value is equal to the expected value content, if not, the credit report is abnormal; If the rule type is the dictionary comparison, it is determined whether the field value belongs to the dictionary enumeration of the expected value content, and if it does not belong to the dictionary enumeration of the expected value content, the credit report is abnormal; If the rule type is the digital range comparison, then determine whether the field value belongs to the digital range listed in the expected value content, if not, then the credit report is abnormal; If the rule type is the format comparison, it is determined whether the field value matches the regular expression corresponding to the expected value content. If not, the credit report is abnormal.

6. A file content abnormality monitoring system, characterized in that: The system comprises: an identification module, a calling module and a judgment module; The identification module is used to obtain a credit report in a target format, identify table field information in the credit report, and determine the table field to be monitored and the field value corresponding to the table field to be monitored from the table field information according to a preset table field location path; The calling module is used to call the rule configuration information based on the rule engine configuration, determine whether the rule configuration information is within the rule effective time of the rule configuration information, and if so, query the preset monitoring rule corresponding to the field of the table to be monitored from the rule configuration information; The judgment module is used to judge whether the credit report is abnormal according to the preset monitoring rules and the field value.

7. The system according to claim 6, characterized in that The system further comprises: a format conversion module; The format conversion module is used to obtain an initial credit report in an initial format, determine whether the initial format is the same as a target format, and if so, determine the initial credit report as a credit report in the target format; if not, convert the initial format into the target format to obtain a credit report in the target format.

8. The system according to claim 6, characterized in that The system also includes: a rule engine configuration module; The rule engine configuration module is used to respond to the monitoring rule configuration instruction, receive the preset monitoring rules corresponding to each field in the table to be monitored, configure the field in the table to be monitored and the corresponding preset monitoring rules, wherein the preset monitoring rules include: rule type, expected value content and the maximum number of times the preset monitoring rules are used, and the rule types include: equivalence comparison, dictionary comparison, numeric range comparison and format comparison, based on the rule engine configuration rule configuration information, wherein the rule configuration information includes each field to be monitored and the preset monitoring rules corresponding to the field in the table to be monitored, and set the rule effective time of the rule configuration information.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring abnormal file content described in any one of claims 1 to 5 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method for monitoring abnormal file content described in any one of claims 1 to 5 is implemented.

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